
-
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
This work introduces lightweight extensions to the RISC-V ISA to boost t...
read it
-
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
The main design principles in computer architecture have recently shifte...
read it
-
An Energy-Efficient Low-Voltage Swing Transceiver for mW-Range IoT End-Nodes
As the Internet-of-Things (IoT) applications become more and more pervas...
read it
-
A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference
Low bit-width Quantized Neural Networks (QNNs) enable deployment of comp...
read it
-
A transprecision floating-point cluster for efficient near-sensor data analytics
Recent applications in the domain of near-sensor computing require the a...
read it
-
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extrem...
read it
-
Performance-Aware Predictive-Model-Based On-Chip Body-Bias Regulation Strategy for an ULP Multi-Core Cluster in 28nm UTBB FD-SOI
The performance and reliability of Ultra-Low-Power (ULP) computing platf...
read it
-
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node
Binary Neural Networks (BNNs) have been shown to be robust to random bit...
read it
-
Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices
The deployment of Quantized Neural Networks (QNN) on advanced microcontr...
read it
-
A 0.5GHz 0.35mW LDO-Powered Constant-Slope Phase Interpolator with 0.22% INL
Clock generators are an essential and critical building block of any com...
read it
-
Arnold: an eFPGA-Augmented RISC-V SoC for Flexible and Low-Power IoT End-Nodes
A wide range of Internet of Things (IoT) applications require powerful, ...
read it
-
Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters
The steeply growing performance demands for highly power- and energy-con...
read it
-
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
We present PULP-NN, an optimized computing library for a parallel ultra-...
read it
-
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine
Deep neural networks have achieved impressive results in computer vision...
read it
-
Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes
Deep neural networks have achieved impressive results in computer vision...
read it
-
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs
Deep convolutional neural networks (CNNs) obtain outstanding results in ...
read it
-
A Transprecision Floating-Point Platform for Ultra-Low Power Computing
In modern low-power embedded platforms, floating-point (FP) operations e...
read it
-
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual ...
read it
-
Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes
High-performance computing systems are moving towards 2.5D and 3D memory...
read it
-
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, a...
read it
-
A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices
Endpoint devices for Internet-of-Things not only need to work under extr...
read it
-
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Convolutional neural networks (CNNs) have revolutionized the world of co...
read it