BRISC-V: An Open-Source Architecture Design Space Exploration Toolbox

08/27/2019 ∙ by Sahan Bandara, et al. ∙ Boston University 0

In this work, we introduce a platform for register-transfer level (RTL) architecture design space exploration. The platform is an open-source, parameterized, synthesizable set of RTL modules for designing RISC-V based single and multi-core architecture systems. The platform is designed with a high degree of modularity. It provides highly-parameterized, composable RTL modules for fast and accurate exploration of different RISC-V based core complexities, multi-level caching and memory organizations, system topologies, router architectures, and routing schemes. The platform can be used for both RTL simulation and FPGA based emulation. The hardware modules are implemented in synthesizable Verilog using no vendor-specific blocks. The platform includes a RISC-V compiler toolchain to assist in developing software for the cores, a web-based system configuration graphical user interface (GUI) and a web-based RISC-V assembly simulator. The platform supports a myriad of RISC-V architectures, ranging from a simple single cycle processor to a multi-core SoC with a complex memory hierarchy and a network-on-chip. The modules are designed to support incremental additions and modifications. The interfaces between components are particularly designed to allow parts of the processor such as whole cache modules, cores or individual pipeline stages, to be modified or replaced without impacting the rest of the system. The platform allows researchers to quickly instantiate complete working RISC-V multi-core systems with synthesizable RTL and make targeted modifications to fit their needs. The complete platform (including Verilog source code) can be downloaded at



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1. Introduction

Designing, building, and testing multi-core, many-core and even single-core processor systems is a difficult and time consuming task. Designers are faced with numerous design decisions that, when taken as a whole, impact performance in subtle ways. With the ever increasing size and complexity of multi-core and many-core systems, the time and effort needed for development is quickly raising the barrier to entry for design space exploration and research. This growing obstacle to multi-core system design creates a need for a flexible micro-architecture design space exploration platform. However, there are many challenges involved with creating such a platform. Salient research questions related to creating such a platform include: (1) What aspects of a multi-core system are relevant to micro-architecture design space exploration? (2) How can a design space exploration platform provide ease of use and rapid exploration while maintaining the speed and accuracy of FPGA-based emulation? (3) How can the size of the design space covered by a platform be maximized to provide support for a wide range of systems and research?

To address these questions and challenges, we present an open-source platform for RISC-V multi-core system micro-architecture design space exploration. The key components of this platform are:

  • A modular, parameterized, synthesizable multi-core RISC-V hardware system written in Verilog.

  • A RISC-V toolchain to compile a user’s code for bare-metal execution on the hardware system.

  • A RISC-V assembly simulator to test a user’s software independently of the hardware system.

  • A hardware system configuration graphical user interface to visualize and generate multi-core hardware systems.

The name of the platform is withheld to maintain anonymity.

Current research that would benefit from fast micro-architecture design space exploration ranges from the development of efficient network on-chip (NoC) (Taheri et al., 2017) to cache timing side channel elimination (Hosseinzadeh et al., 2017) to heterogeneous (Kumar et al., 2004) or adaptive architecture design (Hoffmann et al., 2012). Indeed, there is still active research related to every subsystem in a multi-core design. As such, our platform supports design space exploration for: (1) RISC-V cores, with various pipeline depths and configurations; (2) the cache subsystem with user selectable sizes and associativities; (3) a main memory subsystem with support for on-chip block RAM (BRAM) or off-chip memory; and (4) a parameterized on-chip network supporting a variety of router designs and routing algorithms.

The hardware system is written in synthesizable Verilog with no vendor specific IP blocks. This implementation enables RTL simulation, in addition to the fast FPGA-based emulation necessary for large, yet accurate, design space exploration. Parameterization and modularity allow for rapid changes to the design of the system. Individual modules (e.g. core pipeline stages or cache replacement policies) as well as whole subsystems (e.g. core, cache or NoC designs) can be customized or replaced independently of each other. This allows users to make changes to their relevant systems without the need to modify or understand implementation details of other aspects of the system. The platform provides multiple implementations of core, cache, memory and NoC subsystems for users to choose from. Parameterized modules allow users to quickly change settings of the system, such as cache size, cache associativity, or number of cores. Such parameters enable the fine tuning of a micro-architecture after the appropriate hardware modules have been selected.

The software tools included in the platform facilitate rapid design space exploration by streamlining the platform work flow. Including the necessary toolchain allows users to develop software for their design space exploration quickly and run it on a variety of system configurations. The platform’s RISC-V assembly simulator can be used to create a golden model of program execution and fine tune software before testing it on the hardware system. These golden models can accelerate debugging efforts by providing an expected execution flow. The hardware system configuration GUI allows users to select core types and features, cache sizes, and associativities, bus-based or NoC- based interconnects, among many other parameters. Visualizations of the configuration promptly give users an understanding of their system. Users can use the GUI to generate an RTL implementation of the system, allowing them to easily make and visualize changes, before producing a new design. The modularity that comes with support for interchangeable core, cache, memory, and NoC subsystems (and their internal modules) enables a wide breadth of design space for exploration, without any RTL modifications to the base platform. In order to maximize the explorable design space, every aspect of the platform is open-source. Users looking to expand the number of supported subsystems or add experimental features can do so by modifying or extending the Verilog RTL of the base system.

The use of RISC-V, an open ISA that is freely available, adds additional possibilities for design space exploration with its modular nature and numerous extension specifications (Waterman and Asanovic, 2017) (Waterman and ASANOVI C, 2017). Users can add custom instruction set extensions to support experimental architecture features or custom hardware accelerators. The openness and the option for custom extensions makes RISC-V an excellent ISA for design space exploration. This platform uses version 2.2 of the RISC-V User-Level ISA (Waterman and Asanovic, 2017)

The complete platform (including Verilog source code) can be downloaded at

2. Related Work

Other work has developed configurable processors and tools to facilitate varying degrees of design space exploration. One such tool, Heracles (Kinsy et al., 2013) (Kinsy et al., 2011), is based on the MIPS ISA and runs on the Windows operating system. Everything needed to create a synthesizable system is included with Heracles. A GUI enables users to specify their system and generate Verilog code for it. Two core types, each with one or two hardware threads, can be selected for use in multi-core systems. A dummy core is included to test different on-chip network designs. Cache hierarchies can involve one or two levels of direct mapped caches. A cross compiler allows programmers to write parallel code for a multi-core MIPS architecture. A wide variety of NoC routing configuration options are available in the NoC configuration of Heracles.

Heracles and our platform share similar goals, however, Heracles is more limited in the number of core and cache configurations available. Additionally, the use of RISC-V in our platform makes extending the ISA easier, given RISC-V’s opcodes dedicated to user defined instructions (Waterman and Asanovic, 2017). These improvements in our platform create a richer and larger explorable design space.

The Soft Processor Rapid Exploration Environment (SPREE) tool provides another MIPS-based design space exploration tool (Labrecque et al., 2007) (Yiannacouras et al., 2006). SPREE explores such trade-offs in micro-architectural details as pipeline depth, hazard detection implementation, as well as ISA features like branch delay slots and application-specific register management. However, the design space covered by SPREE is limited to the cores in a processing system. Additionally, only single core designs are supported by SPREE. Our platform offers a more complete design space exploration, with support for multi-core systems with core, cache, memory and NoC configuration options.

The free and open nature of RISC-V means that numerous open-source implementations are available. A few of these, for example, a size optimized core named PicoRV32 (Wolf, 2018) and a Linux capable core named RV12 from RoaLogic (RoaLogic, 2018), include SoCs generated with the Rocket Chip Generator (Asanovic et al., 2016). Many more implementations exist, but for brevity they are omitted. While the RISC-V implementations mentioned offer some level of configuration, none of them support multi-core, cache, or NoC design space exploration in the way our platform does. Table 1 compares the configuration options and features available for design space exploration in each of the referenced works.

angle=45,lap=0pt-(1em)PicoRV32 angle=45,lap=0pt-(1em)RV12 angle=45,lap=0pt-(1em)Rocket Chip angle=45,lap=0pt-(1em)SPREE angle=45,lap=0pt-(1em)Heracles angle=45,lap=0pt-(1em)Our Platform
Extensible ISA
Table 1. A comparison of configurable subsystems and features available in popular configurable processors.

3. Platform Overview

A typical workflow in the platform (shown in Figure 1) starts with developing the software application to be run on the hardware system. Software is compiled with either the GNU or LLVM compiler toolchain for RISC-V. The included compiler scripts support simple multi-processing and multi-threading environments.

After developing the application, users can determine performance requirements as well as power and area constraints. Given the system requirements, a user can begin setting parameters for the core, cache, memory and NoC subsystems. Users can set these parameters with the hardware configuration GUI discussed in Section 8.1. Users requiring a small processor to handle embedded applications might select a small single cycle core with a simple BRAM memory, while a user developing a large distributed multi-core system could opt for the more complex pipelined or out-of-order cores, with large caches and a memory controller for off-chip memory. Users developing a system for a single application can optimize the cache subsystem by selecting line size, associativity, and number of line values best suited to the memory access pattern of the application. For instance, if the application has high spatial locality, then a larger line size can be selected. If users are designing a large many-core processor they can experiment with different NoC topologies and routing algorithms to determine which one gives the best performance for their constraints.

The test benches included in the hardware system can be used to simulate the processing system and verify that the user’s program executes correctly. Tests for the sample programs included in the toolbox have been automated to report a pass or fail result to accelerate development of custom features. After passing simulation tests, the hardware system can be synthesized for implementation on an FPGA. The ability to generate a processing system rapidly allows users to iterate their design in the event that their requirements are not met by the initial system. At any point in the design flow, users can easily go back and tweak the design to meet their constraints better. Quickly iterating a design enables users to develop the hardware and software systems together, facilitating a thorough design space exploration.

Figure 1. A typical workflow for the platform.

4. Platform Core Descriptions

A single cycle processor is presented as a baseline for processor design, emulation, and analysis. For users to explore the impact pipeline depth has on performance and area, the platform includes five- and seven-cycle pipelined processors. For users to explore instruction extensions and a wide variety of micro-architecture features, the platform includes a super-scalar out-of-order processor.

4.1. Single Cycle Processor

The single cycle processor implements the RV32I instruction set with modules designed around the “textbook” fetch, decode, execute, memory, and write-back stages of a processor (Patterson and Hennessy, 2013). This processor serves as the base for other cores; as such, it has been designed to be as simple as possible. The modules in the single cycle processor are reused or wrapped with additional logic to support features such as pipelining and data forwarding. A block diagram of the processor is shown in Figure 2. The single cycle processor has instruction and data memory interfaces compatible with every cache and memory subsystem provided in the platform. Due to the single cycle operation, NOPs are inserted between BRAM or off-chip memory accesses. An asynchronous memory is provided to avoid NOPs, but it cannot be implemented in FPGA BRAM and must be kept small to prevent the memory from using too many device resources.

By using the Verilog hex output from the included compiler toolchain described in Section 8.2, users are able to compile a bare-metal C program and run it on an FPGA implementation of the processor. Synthesis results have been collected and are shown in Table 4. Note that the Logic Element usage is high because the memory is implemented in Look Up Tables (LUT), because of the asynchronous memory system used.

Figure 2. RV32I single cycle core.

4.2. Five Stage Pipeline

The five stage processor is implemented by using the base modules from the single cycle processor as a starting point and adding pipeline registers between the combinational fetch, decode, execute, memory and writeback modules. The single cycle control logic module is wrapped with additional logic to support the stall and bypass signals needed for pipelining. Introducing multiple instructions in flight demonstrates how hazard resolution must consider bypassing, stalling, and pipeline flushing.

Pipelining allows for a higher clock frequency; however, NOPs are still inserted between synchronous memory operations because there is no pipeline register between the address input and data output of the memory interfaces. Asynchronous memories must still be used to avoid NOPs between instruction fetches. Note, however, that the addition of pipeline stages allows some synthesis tools to implement an asynchronous main memory (without caches) in FPGA BRAM. The five-stage pipelined processor has two variants. The first uses only pipeline stalls and flushes when a pipeline hazard is detected. The second implements data forwarding to avoid stalls for most hazards. Stalling and forwarding logic is wrapped around the base control unit used in the single cycle core. A multiplexer is wrapped around the decode logic to output forwarded data when needed. Wrapping the base modules to build the five stage pipeline maximizes IP reuse and allows for user base module customizations to be carried through their core design space.

Figure 3. RV32I five-stage pipelined core.

4.3. Seven Stage Pipeline

The seven-stage pipelined processor builds on the base modules in the single cycle processor and the pipeline related modules introduced with the five stage processor. It adds registers between the address inputs and data outputs of the memory interfaces to avoid inserting NOPs while waiting for synchronous memory operations. With these extra pipeline stages, BRAM reads and cache hits no longer need to insert NOPs. The additional pipeline stages can be seen in the seven stage pipeline block diagram shown in Figure 4.

Placing extra stages between the memory interface input and outputs enables logic to check that a memory read is valid while the next operation is issued. Operations in cacheless implementations with on-chip BRAM will always be valid; however, the addition of caches means that cache misses could delay valid read data. In the event of a cache miss, the received data is marked invalid by the memory and the processor stalls until the requested memory has been retrieved. On cache hits, execution continues normally with no inserted NOPs. The extra registers in the seven stage pipeline yield an improved maximum clock frequency. The extra pipeline registers lead to simplified control logic, resulting in a slightly reduced area. Synthesis results for the seven stage pipeline are shown in Table 4.

Figure 4. RV32I seven-stage pipelined core.

4.4. Out of Order Processor

The Out-Of-Order (OOO) core enables exploration of advanced architectural features including a superscalar architecture, instruction scheduling, and complex hazard resolution. The OOO processor supports out-of-order execution with in order commit. The number of ALUs has been parameterized to allow users to explore the impact of a varying number of functional units on processor performance.

The OOO processor implements the RV32F instruction set extension to create more opportunities for out-of-order execution. In order to support the floating point extension, a floating point register file and floating-point execution units were added to the processor. The base decode and control units have been expanded with extra logic to support the new floating point instructions.

The OOO core adds three multi-cycle modules to the processor pipeline: (1) an instruction queue, (2) a scheduler, and (3) a commit stage. These three stages do not reuse any of the base modules from the in-order cores discussed previously. Figure 5 shows a block diagram of the OOO micro-architecture.

In the out-of-order processor, instructions are fetched and decoded before being placed in the new instruction queue stage. The instruction queue length can be modified by the user to trade off performance and area. The queue is implemented as a priority queue, in which the highest priority (longest waiting) instruction without any hazards is scheduled next.

The scheduler module supports a parameterized number of floating point and integer ALUs. When both an instruction and an ALU are ready, the scheduler assigns the instruction to the available ALU. After the instruction has completed its execution with respect to the ALU, it continues through the pipeline into the commit stage. Up to one instruction is scheduled each clock cycle. ALUs can be pipelined to ensure their utilization remains high, offering yet another design space exploration trade off.

Memory operations wait in the commit module until the memory access has completed. Instructions in the commit module are buffered and sent to the writeback unit in order. Instructions are re-ordered based on the priorities used in the instruction queue. An instruction’s ‘rd’ value is stored in a table in the instruction queue stage. Hazards are detected by comparing the ‘rd’ values of in-flight instructions to the ‘rs1’ and ‘rs2’ values of instructions entering the queue. An instruction’s ‘rd’ value is cleared from the table in the instruction queue stage after the instruction exits the writeback stage, completing its execution.

Figure 5. RV32IF Out-of-order core.

5. Cache subsystem

Our platform includes a highly configurable multi-level cache subsystem in order to provide a high degree of freedom to users. Different cache configurations can be implemented by adjusting parameters. The cache subsystem can be easily modified to fit different performance requirements or available resources. For instance, if the system is to be implemented on a smaller FPGA, one or two levels of smaller caches could be used. Alternatively, if a larger FPGA is available, larger caches and more levels in the cache hierarchy would provide better performance. The ability to test a large number of different cache configurations, without investing time to develop the different cache systems from scratch, streamlines cache-focused design space exploration.

The cache subsystem supports multi-stage inclusive caches. The caching policy is “write back with write allocate”. Currently, the cache system supports “MESI” cache coherence. Other system parameters, such as the number of cache levels, cache size, and internal parameters of each cache are configurable. Coherent cache configurations with heterogeneous line widths and associativities are also supported. The cache subsystem is comprised of two fully parameterized cache modules, a shared bus, and a coherence controller. An interface module is included to act as the interface between the last level cache and the main memory or on-chip network.

The “main_memory_interface” module bridges the gap between main memory word size and last level cache line size. This decoupling allows the user to use off-chip memory as the main memory. In the case of a distributed memory system, this module also acts as the interface between the last level cache and the on-chip network. Figure 6 depicts the platform’s cache hierarchy.

Figure 6. Multi-core memory hierarchy block diagram.

5.1. L1cache

The “L1cache” module is designed to be used as the level 1 cache that interfaces with a processor. It provides user configurable parameters for cache size, cache line width, associativity, and replacement policy. The “INDEX_BITS” parameter determines the number of cache sets. “OFFSET_BITS” parameter determines the cache line width while “NUMBER_OF_WAYS” parameter specifies the associativity of the cache. RTL for the cache is written in a highly parameterized fashion with additional parameters for data width, address width, number of status bits, number of coherence bits, number of bits to select replacement policy, and number of bits used for communication with the other levels of the cache hierarchy. These parameters make it simpler to build extensions to the cache subsystem by minimizing the number of RTL changes required.

The “L1cache” receives six signals from the processor. The processor specifies the memory address to access with the “address_in” port and the data to write with the “data_in” port. Additional ports exist for the read, write, flush, and invalidate signals. Flush and invalidate operations are carried out on a cache line granularity. When the processor specifies a single memory address with the flush/invalidate signal, the cache line containing the address is
flushed/invalidated throughout the entire cache hierarchy. If the cache line was dirty, it is written back to lower levels in the cache hierarchy and ultimately to main memory. Since the caches are inclusive, a flushed cache line is also flushed/invalidated from L1 caches of other cores in the system.

The L1 cache sends four signals to the processor. The “data_out” bus sends the data read from memory, while the “out_address” is the memory address corresponding to the data on the data bus. The “valid” signal indicates that the data on the data bus is valid. The “ready” signal informs the processor that the cache is ready for the next memory request. The processor should stall parts of the pipeline based on “valid” and “ready” signals. Both signals are required to convey the cache status to the processor because the L1 cache operates in a pipelined fashion with up to two accesses in flight at any time. The L1 cache has a one-cycle access time due to FPGA BRAM access latency. Each way of the cache is mapped to a separate BRAM on the FPGA.

Both “L1cache” and “Lxcache” modules use the same interface for communicating with the lower levels (caches more distant from the processor) in the cache hierarchy. A common interface is used to enable an arbitrary number of cache levels in a design. This interface consists of six signals: data_in, address_in, message_in, data_out, address_out and message_out.

Data in/out contains a whole cache line. All caches use different 4-bit messages to communicate with other caches via the shared bus. There are two types of messages. The first type is to communicate basic requests such as read, write back, flush, etc.. The second type of messages is related to cache coherence. When an L1 cache is writing to a shared line or reading a cache line on a write miss, it broadcasts its intent so that the other caches can perform necessary coherence operations. L2 cache issues flush requests to L1 caches when evicting a cache line that one or more L1 caches have a copy of. Cache coherence is discussed in detail in Section 5.3.

Currently, the caches can be configured to perform true Least Recently Used (LRU) or random replacement of cache lines. Because of the modular design of the “replacement_controller” module, a user can easily implement other replacement policies.

5.2. Lxcache Module

The “Lxcache” module is configurable to be used at any level in the cache hierarchy except for level 1 where the cache interfaces with the processor. “Lxcache” supports all the configurable parameters in “L1cache”. The “Lxcache” module adds the capability to serve an arbitrary number of ports with round robin arbitration. Multiple ports enable level 1 caches from several processors to be connected to a shared level 2 cache.

5.3. Cache coherence

The platform’s cache subsystem implements “MESI” cache coherence which makes it possible to build and test multi-core architectures. The “coherence controller” module is designed to be instantiated alongside L1 and L2 caches. This module is capable of serving an arbitrary number of L1 caches and the L2 cache. The shared bus between L1 and L2 caches is controlled by the coherence controller. It listens to messages issued by L1 and L2 caches and controls which cache drives the shared bus.

The L1 caches are designed to listen and respond to messages on the shared bus by either writing back or invalidating cache lines. Different operations, of which a read request is the simplest, trigger cache coherence operations. If one of the other caches has a dirty copy (in “MODIFIED” state) of the requested cache line, it will write back the dirty cache line to the shared bus. The coherence controller will allow the write back to go on the bus so that the L1 cache that issued the original read request and the L2 cache can update the cache line in question. If any of the caches have “exclusive” copies of the cache line, they will be changed to the “SHARED” state.

When an L1 cache is writing to a shared cache line, it sends a message communicating its action. That message is broadcasted to all other L1 caches over the shared bus. Upon receiving the message, other caches invalidate the shared line and respond to the coherence controller. Once all caches respond to the broadcast, the coherence controller puts “NO_REQ” message on the bus. This indicates to the first cache that it can write to the shared line and change its coherence status to “MODIFIED”.

Another type of coherence operation is triggered when an L1 cache encounters a write miss. Since it intends to write to the cache line read from the lower level, the L1 cache sends a message requesting ownership of the cache line instead of a standard read request. This indicates to the other L1 caches that unlike with a read request, they should invalidate the cache line if they have copies of it in “SHARED” or “EXCLUSIVE” states.

The last type of coherence operation occurs when the L2 cache is about to evict a cache line that is also in one or more of the L1 caches. Since this is an inclusive cache hierarchy, the copies of the cache line should be evicted form L1 caches as well. Therefore, the L2 cache issues a flush request to the L1 caches.The L1 caches either invalidate or write-back the cache line depending on its status.

L1 caches are designed with an independent snooper module to perform coherence operations. Dual ported block RAMs are used as memory to perform coherence operations without interrupting normal memory accesses by the cache controller. When both the cache controller and the snooper access the same cache line, the coherence operation is given priority. Bus interface also gives the snooper priority when both the controller and snooper attempt to access the shared bus.

5.4. Limitations

Currently, cache coherence is handled at the first level of the cache hierarchy (L1 caches). This forces the L2 to be shared between all processors. Future improvements to the cache subsystem will allow the user to choose whether cache coherence is handled at L1 or L2 level. Moving the coherence controller to the L2 level will allow a user to build a cache hierarchy with private L1 and L2 caches, and a shared L3 cache.

6. Main memory and Network-on-chip

The main memory interface decouples the cache subsystem and the main memory. Users have the option to use any of the provided main memory subsystems: (1) unified or separate asynchronous instruction and data memory, (2) unified or separate synchronous instruction and data memory, or (3) off-chip memory controller.

Connecting the main memory interface to an off-chip memory controller enables large main memories. Using off-chip memory is useful when a system needs more memory than is available on a given FPGA. Currently the platform includes a simple off-chip SRAM memory controller; other device specific memory controllers can easily be added. The interface also supports connections to an on-chip network, which, coupled with the ability to configure the size of main memory on a per node basis, enables uniform and non-uniform distributed memory systems.

7. On-Chip Network

The platform’s on-chip network works with the memory subsystem to implement a variety of multi-core architectures. The NoC provides a number of configuration options, enabling the user to explore different network topologies and optimize the resource usage and performance of the system. The network can be configured to explore different combinations of: 1) flow control, 2) routing algorithms, and 3) network topologies. The NoC router is fully parameterized 7.

Routers in the NoC can be configured as buffered or buffer-less routers. The routers support oblivious routing algorithms using fixed logic or configurable routing tables. Fixed logic is implemented for Dimension Order Routing. Programmable routing tables enable different routing algorithms with changes to the routing table entries. A wide range of network topologies can be be implemented by configuring the number of input ports, output ports and routing table contents of the routers. Routers included in the platform are conventional virtual channel routers. There are single cycle and pipelined variants of the routers. Users can configure different parameters such as number of input/output ports, virtual channels per port, virtual channel depth to tune the performance and resource usage of the on-chip network opening a rich design space for exploration. The on-chip network is based on the NoC included with the Heracles system (Kinsy et al., 2011) (Kinsy et al., 2013).

Figure 7. NoC router architecture.

8. Workflow Description

Being able to make changes to the hardware system easily and understand the performance implications of those changes make our platform a powerful micro-architecture design space exploration tool. Quick design changes are supported with open-source parameterized Verilog modules and a hardware system configuration GUI. The configuration GUI provides a user friendly way to choose parameters and visualize a hardware system. A compiler toolchain streamlines software development. A RISC-V GCC cross-compiler binary is included, so users do not have to configure and build the RISC-V tools from source code. The following subsections describe the workflow for the software toolchain and hardware configuration GUI.

8.1. Hardware Configuration GUI

The hardware system configuration application is a graphical application that allows users to configure a hardware system to meet their specification. The application runs in a web browser, allowing users to run it on Windows, Linux, or Mac.

With the hardware configuration system users can (1) select their desired core type and features; (2) include a cache subsystem, if desired, and select its parameters; (3) choose a main memory subsystem, e.g. on-chip, off-chip, unified or separate instruction and data memories; and (4) choose NoC configuration options including number of routers, router topology, and router scheme.

Figure 8 shows a screenshot of the application. On the left are menu and parameter entry text boxes. A block diagram of the configured system is shown on the right of the application window. Selecting different menu options opens different parameter selection tabs. The five core types currently included are 1) single cycle, 2) five-stage pipeline with stall on hazard, 3) five-stage pipeline with data forwarding, 4) seven-stage pipeline with forwarding, and 5) pipelined Out-Of-Order. As the processor cores gain complexity, so do their parameters. The cores are built off of one another, with each core serving as the starting point for the next more complex core. Each new implementation supports the previous processor’s parameters, in addition to any new required parameters. If on-chip main memory is selected, it can be initialized with the “PROGRAM” parameter. This parameter points to a Verilog Memory Hex file that is output by the provided compiler toolchain described in Section 8.2.

After a user has configured the system, clicking “Generate and download” will download the configured RTL and selected binary from the application. Note that the application is run in a web browser but the entire application can also be local to a user’s machine. No internet connection is required to use the configuration GUI. The use of “Download” here refers to the fact that the browser is oblivious to the application’s origin. Running the hardware configuration GUI in a browser enables researchers, students and teachers to use it locally on a platform of their choice or host it on a server for users to access remotely. Hosting the configuration GUI on a server could simplify its use in a classroom environment.

Figure 8. A screenshot of the hardware configuration GUI. Note that logos have been cropped out to maintain anonymity.

8.2. Compilation

Software for the platform can be built using the standard GNU (10) or LLVM (9) compiler toolchains. The GNU toolchain is the default and is distributed in the binary form with the project. The binary distribution includes compiler, assembler, linker and the standard library. For users who decide to use LLVM toolchain, we distribute detailed instructions for building a bare-metal RISC-V LLVM backend. RISC-V is supported as an experimental target from LLVM version 8.0.0 and can easily be enabled during the build process. Beside support for modern programming languages such as Rust, LLVM infrastructure provides modular mechanisms for adding custom instructions and compiler optimizations. The educational material that covers the writing of compiler backends and custom optimizations in the form of “passes” is publicly available. These features of the LLVM infrastructure are beneficial for design space exploration.

A script is included to compile user code and convert it to a format that can be synthesized as ROM or initialized RAM for implementation on an FPGA. The provided compilation script outputs (among other formats) an ASCII encoded Verilog Memory Hex (.vmh) file. This .vmh file can be used to initialize memory contents on an FPGA with the Verilog $readmemh() function.

The provided compilation script can be found in the “software” directory. Application source code should be placed in the “software/applications/src” directory. In order to compile an application, the user runs the “compile” script from the software directory. Figure 9 shows the command usage and output.

Figure 9. Example Shell command to compile a program named short_mandelbrot.c with an initial stack pointer of 2044 (0x7fc).

The first argument to the compile script is the name of the C file that user wants to compile. The remaining arguments are optional. The second argument sets the initial stack pointer. The third argument sets the size of the stack given each hardware thread (HART) and a fourth argument specifies the number of harts in the system. By default the stack pointer is 2044 (0x7fc) and only one HART is used. The stack pointer may need to be changed if the selected memory size is very small. The compilation script places the resulting .vmh file in the “software/applications/binaries/” directory alongside with the other outputs.

The project currently does not support any operating system and applications must be run in a bare-metal environment. To eliminate any extra setup by the user to enable bare-metal execution of their code, the compiler script wraps the user application with assembly code to initialize each of the registers and call the main function. The assembly wrapper executes an infinite loop after the main function returns. This wrapper can be thought of as a simple kernel. Multiple HARTs can be used by writing a function named hartN_main() for each HART, where “N” is the HART number. The simple kernel will call each HART’s main function.

The user’s custom compiler choice as well as the compiler options should be provided as environment variables. The environment variable used to supply additional options to GCC compiler is “GCC_OPTS”. The variables to control the version of LLVM compiler and the additional options are “LLVM” and “LLVM_OPTS”, respectively. The user should provide these options before they run the compilation script. That being so, we maintain the flexibility of the compilation process and support the standard tools for code optimizations.

After running the provided compile script, a user can set the “PROGRAM” parameter to the path of their .vmh file (manually or in the hardware configuration GUI) and synthesize their design. The instruction memory of the processor will be initialized with the contents of the binary if it is on-chip. The instruction memory also has a port for writing new programs after the design has been configured on an FPGA.

9. Platform Evaluation

9.1. Stress Testing Example of the Platform

Figure 10. Incorrectly (a) and correctly (b) rendered Mandelbrot set.

The different versions of the processing cores in this platform lay the groundwork for a quick micro-architecture design exploration. The cores included in this platform provide various levels of complexity for users to build on. In order to evaluate the ease of use of the platform, we instantiate a simple faulty core and add peripheral devices such as a memory mapped VGA frame buffer. The frame buffer is used to render a Mandelbrot fractal. Testing the rendering program revealed that the fault was injected in the ALU module and caused it to execute signed operations incorrectly. Figure 10 compares the incorrect rendering with the correct rendering. Finding and fixing this bug is an example of how this design space exploration platform enables users to make additions easily and expand core designs.

9.2. Design Space Exploration

To evaluate the performance of the platform and showcase the extent of design space exploration possible, we benchmark several processing system configurations. First, we compare single cycle, five-stage pipeline (with and without data forwarding) and seven-stage pipeline (with data forwarding) cores with asynchronous memory in the single cycle system and synchronous memory in the pipelined systems. Each system uses a dedicated instruction and data memory module in a single core configuration. These configurations do not use caches to avoid paying the penalty of cache misses without the benefit of larger off-chip main memory. Second, we compare multi-core systems connected via a bus between the level 1 and level 2 caches. In each multi-core system, the seven-stage pipelined core is used.

To compare the single-core, cacheless processor configurations, three different benchmark programs are run on each core. One benchmark computes the factorial of an integer. The second benchmark counts the number of prime numbers between two numbers. A third benchmark computes the Mandelbrot set at a given resolution and checksums the result.

Figure 11. (a) Runtime of each single-core system for each benchmark, normalized to the single cycle core runtime. (b) Runtime of single, dual, quad and octa-core processors for the prime number counting benchmark, normalized to the single core runtime.

Each benchmark is executed in an RTL simulation of the configured processing system. The number of cycles needed to complete the program execution is recorded. The estimated

(obtained from synthesis tools) of each core and the number of clock cycles in each program execution is used to compute the runtime of the benchmark. Table 2 reports the number of cycles for each benchmark. Figure 11 compares the runtime of each benchmark on each of the configured systems. The runtimes are normalized to the single cycle core’s runtime.

The single cycle system executes each benchmark in the fewest cycles but the low clock frequency hurts the program runtime. The single cycle core’s clock frequency is roughly half that of the other cores because the asynchronous main memory is implemented in logic elements instead of the faster BRAMs.

Core Clock Prime Factorial Mandelbrot
Type Freq. Cycles Cycles Cycles
Single Cycle 29.0 MHz 3,464k 59k 488k
5 Stage Stalled 62.6 MHz 11,789k 190k 1,654k
5 Stage Bypassed 61.5 MHz 5,891k 107k 833k
7 Stage Bypassed 81.1 MHz 6,833k 113k 948k
Table 2. Clock frequency and number of clock cycles to run each benchmark for each configured single core system.
Core Clock Number of
Count Frequency Cycles
1 61.7 MHz 7,995,845
2 59.5 MHz 4,373,431
4 60.0 MHz 2,671,258
8 59.5 MHz 1,426,346
Table 3. Clock frequency and number of clock cycles to run the prime counting benchmark for different number of cores.

The five-stage pipelined processor has a much higher clock frequency than the single cycle core, but must stall the pipeline for each hazard encountered. Pipeline stalls lead to a much higher number of cycles needed to execute the program. The five-stage pipeline without data forwarding has the highest program runtime of the tested cores. Adding data forwarding to the five-stage pipeline cuts the program runtime in half, yielding runtimes better than the single cycle core.

The seven-stage pipeline adds two stages to the five-stage pipeline to support synchronous memories without inserting NOPs. These extra stages increase the clock frequency but also increase the number of cycles needed to compute the target address of branch and jump instructions. The five-stage pipeline must wait two cycles before a jump or branch address is ready while the seven-stage pipeline must wait three cycles. Neither pipeline has a branch predictor. The extra cycles spent stalling are canceled out by the higher clock frequency of the seven-stage pipeline. The seven-stage pipeline has the best runtime for each benchmark.

Comparing the five-stage pipeline with and without data forwarding demonstrates the effectiveness of forwarding in resolving pipeline hazards. Comparing the five- and seven-stage pipelines with data forwarding illustrates how the number of bubbles inserted in the pipeline during jumps, branches, and the remaining load-use hazards impacts program runtime. Figure 12 plots the Mandelbrot fractal benchmark runtime versus the core area to visualize the area and performance trade-off. Only the area of the core is considered; area used by memory is ignored here. The area usage of each core is discussed in Section 9.3.

To compare multi-core architectures using caches, the same prime counting benchmark used in the single core tests is parallelized and run on multi-core systems with different core counts. The L1 instruction and data caches in the configured multi-core systems have four 32-bit words per cache line and are 4-way set associative. There are 256 lines in each L1 cache. The shared L2 cache in each processor also has four 32-bit words per cache line and is 4-way set associative. The L2 cache has 512 cache lines. As expected, increasing the number of cores decreases the program runtime. Table 3 shows the clock frequency of each multi-core processor and the number of cycles needed to execute the benchmark program. Figure 11 compares the runtime of each processor. Note that each time the number of cores is doubled, the runtime is nearly halved.

By providing different core options with a range of pipeline depths and clock frequencies, the platform simplifies analysis of performance and area tradeoffs. The analysis presented here was limited to core micro-architecture, cache design and number of cores but many more options are available for fine tuning.

Figure 12. Comparisons of the performance and area trade-offs made by each configured single-core system. Only core area is considered. Area used by memory is ignored.

9.3. Synthesis Results

The runtime lengths presented in Tables 2 and 3 are based on simulations, but each core is fully synthesizable. All synthesis results presented here target an Altera Cyclone IV FPGA with 150k logic elements.

Table 4 shows the “ADDRESS_BITS” parameter value, logic element usage, BRAM usage and worst case for each cacheless core type. In Table 4, the “ADDRESS_BITS” parameter was set to 12 for each pipelined core. Twelve address bits yields 4k 32-bit word addresses for each instruction and data memory (8k words total). The single cycle core uses the asynchronous memory and must implement its memory in logic elements. To ensure the single cycle design fits on the device, it was synthesized with just 1k word memories.

The cores in Table 4 do not use caches. Separate instruction and data RAMs are connected directly to the processors memory interfaces to act as main memory. Using simple memory minimizes the memory system’s impact on resource usage. These results focus on the cores themselves. Tables 67 and 8 show cache resource usage in isolation. Table 5 shows synthesis results for several multi-core processor configurations.

Table 6 shows synthesis results for varying L1 cache sizes targeting the same Cyclone IV FPGA mentioned above. The number of logic elements and BRAM used as well as the maximum clock frequency are reported. Each cache is 4-way set associative and has 4 word (16 Byte) cache lines. The synthesis results show that varying the cache size while maintaining the same line size and associativity only marginally changes the logic element usage and . Only the BRAM usage is significantly impacted by cache size.

Table 7 shows synthesis results for 4kB L1 caches with 4 word (16 Byte), cache lines and various associativities. Intuitively, the number of logic elements grows with the associativity while shrinks. The BRAM usage grows slowly with the associativity. The 16-way associative cache uses 2k more BRAM bits than the 1-way (direct mapped) cache. The increase in BRAM usage with higher associativities can be explained by the higher BRAM usage in the ‘replacement_controller’ module, which implements the true least recently used (LRU) replacement policy.

Table 8 shows synthesis results for 4kB L1 caches with line widths ranging from 1 word (4 Bytes) to 16 words (64 Bytes). Varying the line width trades off BRAM and logic elements. Smaller line widths use more BRAM bits for tag storage, while larger line widths use more logic elements to implement registers and buses to handle wider cache lines.

Table 5 shows the resource usage for processors with 1, 2, 4 and 8 cores. The cores used in the multi-core processor are versions of the 7-stage pipelined core. The L1 caches, shared L2 cache, and coherence controller modules add a significant area overhead but enable the use of complex memory hierarchies. The significantly larger BRAM usage in the multi-core processors stems from the large cache and on-chip main memory. The memory in the single core processors was made to be smaller because larger memories were not needed for the benchmarks. Note that the BRAM usage for the 8-core processor is slightly greater than what is available on the device. We have included the results here because moving the main memory to off-chip SRAM available on our development board would allow the caches to fit within the device BRAM without significantly impacting the performance.

The memory hierarchy for each of the processors in Table 5 uses the same parameters. 16kB L1 caches, 32kB L2 caches and a main memory of 256kB.

The different cores and cache configurations supported by the platform enable users to examine the difference between design choices quickly. Additional configuration options, including NoC routing, NoC topology, and on/off-chip main memory configurations have not been included here due to space restrictions.

Each core builds off of the same set of base modules, making it easy to integrate experimental features into several of them by modifying only the base module. This extensibility opens up even more possibilities for design space exploration. We have used this technique to add data forwarding, originally added to the five-stage pipeline, to the out-of-order and seven-stage pipeline cores.

Core Addr Logic BRAM Fmax
Type Bits Elements Bits
Single Cycle 10 53,448 0 29.0 MHz
5 Stage Stalled 12 3,160 262,144 62.6 MHz
5 Stage Bypassed 12 3,406 262,144 61.5 MHz
7 Stage Bypassed 12 3,366 262,144 81.1 MHz

Table 4. Synthesis results for each configured single-core processing system.
Core Logic Total Memory Fmax
Count Elements Bits (BRAM) Fmax
1 12,206 2,736,128 61.7 MHz
2 21,123 3,055,616 59.5 MHz
4 38,921 3,694,592 60.0 MHz
8 83,020 4,972,544 59.5 MHz
Table 5. Synthesis results for each configured multi-core processing system.
Cache Addr Logic Total Memory Fmax
Size Bits Elements Bits (BRAM)
1kB 4 3,208 10,240 79.1 MHz
2kB 5 3,196 20,352 81.0 MHz
4kB 6 3,198 40,448 82.6 MHz
8kB 7 3,189 80,384 81.1 MHz
16kB 8 3,189 159,744 82.8 MHz
Table 6. Synthesis results for various sizes of 4-way set associative L1 caches. Each cacheline is 16 Bytes (four 32-bit words)
Cache Addr Logic Total Memory Fmax
Ways Bits Elements Bits (BRAM)
1 8 2,387 39,424 104 MHz
2 7 2,736 39,936 97 MHz
4 6 3,198 40,448 82 MHz
8 5 4,534 40,960 66 MHz
16 4 6,546 41,472 49 MHz
Table 7. Synthesis results for various associativities in 4kB L1 caches. Each cacheline is 16 Bytes (four 32-bit words)
Line Addr Logic Total Memory Fmax
width Bits Elements Bits (BRAM)
4 Bytes 8 1,663 63,488 82 MHz
8 Bytes 7 2,227 48,128 81 MHz
16 Bytes 6 3,198 40,448 82 MHz
32 Bytes 5 5,039 36,608 81 MHz
64 Bytes 4 8,888 34,688 76 MHz
Table 8. Synthesis results for various line widths in 4kB, 4-way set associative L1 caches.

10. Future Work

The goal of this work is to explore techniques to develop a fast, flexible, multi-core design space exploration platform, enabling users to understand the impact of their design decisions and quickly test different configurations. To expand the number of configuration options available to users further, the authors plan to add several new features to the available cores, including branch predictors, hardware multi-threading, and other RISC-V ISA extensions, such as the floating point (RV32F) or multiply (RV32M) extensions. Each of these new features will be made available as an option for a user’s desired core and incorporated into the hardware configuration GUI.

The platform has already supported, and will continue to support, research relating to secure architectures focused on enabling efficient obfuscation with hardware-software co-design. The improvements described above will be necessary to support additional research focused on developing RISC-V architectures for HPC workloads with efficient distributed memory.

As a design space exploration platform, the explorable design space is only limited by the man-power available to develop configurable features. For this reason, we have released the source code for the entire platform (the RTL, toolchain customizations and GUI application code) in the hope that others in the community can benefit from, and add to the design space exploration capabilities of the tool.

11. Conclusion

This platform works to address the challenge of fast multi-core design space exploration. By offering highly parameterized cores, cache, memory, and NoC subsystems, our platform allows users to quickly explore a RISC-V architectural design space without the need to develop complex multi-core systems from scratch. A supporting hardware configuration application GUI enables rapid selection of system parameters and RTL generation. Once Verilog is generated by the configuration application, users can investigate how each design decision will impact system properties such as performance, area or timing. Users can add custom features or other modifications to further expand the explorable design space.

Including the necessary compiler tool-chain makes running experiments on customized hardware systems simpler. The whole platform system is open source, including all of the RTL code,
toolchain customizations and supporting applications, enabling
users to customize components to fit their needs.


  • K. Asanovic, R. Avizienis, J. Bachrach, S. Beamer, D. Biancolin, C. Celio, H. Cook, D. Dabbelt, J. Hauser, A. Izraelevitz, et al. (2016) The rocket chip generator. EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2016-17. Cited by: §2.
  • H. Hoffmann, J. Holt, G. Kurian, E. Lau, M. Maggio, J. E. Miller, S. M. Neuman, M. Sinangil, Y. Sinangil, A. Agarwal, A. P. Chandrakasan, and S. Devadas (2012) Self-aware computing in the angstrom processor. In Proceedings of the 49th Annual Design Automation Conference, DAC ’12, New York, NY, USA, pp. 259–264. External Links: ISBN 978-1-4503-1199-1, Document Cited by: §1.
  • H. Hosseinzadeh, M. Isakov, M. Darabi, A. Patooghy, and M. A. Kinsy (2017) Janus: an uncertain cache architecture to cope with side channel attacks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Vol. , pp. 827–830. External Links: Document, ISSN Cited by: §1.
  • M. A. Kinsy, M. Pellauer, and S. Devadas (2011) Heracles: fully synthesizable parameterized mips-based multicore system. In 2011 21st International Conference on Field Programmable Logic and Applications, pp. 356–362. Cited by: §2, §7.
  • M. A. Kinsy, M. Pellauer, and S. Devadas (2013) Heracles: a tool for fast rtl-based design space exploration of multicore processors. In Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays, pp. 125–134. Cited by: §2, §7.
  • R. Kumar, D. M. Tullsen, P. Ranganathan, N. P. Jouppi, and K. I. Farkas (2004) Single-isa heterogeneous multi-core architectures for multithreaded workload performance. In Proceedings. 31st Annual International Symposium on Computer Architecture, 2004., pp. 64–75. Cited by: §1.
  • M. Labrecque, P. Yiannacouras, and J. G. Steffan (2007) Custom code generation for soft processors. SIGARCH Comput. Archit. News 35 (3), pp. 9–19. External Links: ISSN 0163-5964, Document Cited by: §2.
  • D. A. Patterson and J. L. Hennessy (2013) Computer organization and design, fifth edition: the hardware/software interface. 5th edition, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. External Links: ISBN 0124077269, 9780124077263 Cited by: §4.1.
  • [9] (2018) RISC-v llvm support. GitHub. Note: Cited by: §8.2.
  • [10] (2018) RISC-v tools (gnu toolchain, isa simulator, tests). GitHub. Note: Cited by: §8.2.
  • RoaLogic (2018) RV12. Cited by: §2.
  • E. Taheri, M. Isakov, A. Patooghy, and M. A. Kinsy (2017) Advertiser elevator: a fault tolerant routing algorithm for partially connected 3d network-on-chips. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Vol. , pp. 136–139. External Links: Document, ISSN Cited by: §1.
  • A. Waterman and K. Asanovic (2017) The risc-v instruction set manual-volume i: user-level isa-document version 2.2. RISC-V Foundation (May 2017). Cited by: §1, §2.
  • A. Waterman and K. ASANOVI C (2017) The risc-v instruction set manual, volume ii: privileged architecture. CS Division, EECE Department, University of California, Berkeley (May 2017). Cited by: §1.
  • C. Wolf (2018) Picorv32-a size-optimized risc-v cpu. Cited by: §2.
  • P. Yiannacouras, J. G. Steffan, and J. Rose (2006) Application-specific customization of soft processor microarchitecture. In Proceedings of the 2006 ACM/SIGDA 14th International Symposium on Field Programmable Gate Arrays, FPGA ’06, New York, NY, USA, pp. 201–210. External Links: ISBN 1-59593-292-5, Document Cited by: §2.