b'Davide Rossi'

research

∙ 05/15/2023

Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30

Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) Sys...

0 Francesco Conti, et al. ∙

research

∙ 05/12/2023

Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays

Emerging applications in the IoT domain require ultra-low-power and high...

0 Mattia Sinigaglia, et al. ∙

research

∙ 03/31/2023

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose s...

0 Angelo Garofalo, et al. ∙

research

∙ 03/29/2023

Cyber Security aboard Micro Aerial Vehicles: An OpenTitan-based Visual Communication Use Case

Autonomous Micro Aerial Vehicles (MAVs), with a form factor of 10cm in d...

0 Maicol Ciani, et al. ∙

research

∙ 02/06/2023

CVA6 RISC-V Virtualization: Architecture, Microarchitecture, and Design Space Exploration

Virtualization is a key technology used in a wide range of applications,...

0 Bruno Sá, et al. ∙

research

∙ 01/10/2023

RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration

The increasing interest in TinyML, i.e., near-sensor machine learning on...

0 Yvan Tortorella, et al. ∙

research

∙ 11/27/2022

HULK-V: a Heterogeneous Ultra-low-power Linux capable RISC-V SoC

IoT applications span a wide range in performance and memory footprint, ...

0 Luca Valente, et al. ∙

research

∙ 11/23/2022

End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture

The demand for computation resources and energy efficiency of Convolutio...

0 Nazareno Bruschi, et al. ∙

research

∙ 08/18/2022

Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs

Small-size unmanned aerial vehicles (UAV) have the potential to dramatic...

0 Alfio Di Mauro, et al. ∙

research

∙ 06/03/2022

Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference

Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm f...

0 Nazareno Bruschi, et al. ∙

research

∙ 04/24/2022

RedMulE: A Compact FP16 Matrix-Multiplication Accelerator for Adaptive Deep Learning on RISC-V-Based Ultra-Low-Power SoCs

The fast proliferation of extreme-edge applications using Deep Learning ...

0 Yvan Tortorella, et al. ∙

research

∙ 03/29/2022

Geographic Diversity in Public Code Contributions

We conduct an exploratory, large-scale, longitudinal study of 50 years o...

0 Davide Rossi, et al. ∙

research

∙ 02/15/2022

Worldwide Gender Differences in Public Code Contributions

Gender imbalance is a well-known phenomenon observed throughout sciences...

0 Davide Rossi, et al. ∙

research

∙ 01/21/2022

Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode

Computationally intensive algorithms such as Deep Neural Networks (DNNs)...

0 Gianmarco Ottavi, et al. ∙

research

∙ 01/04/2022

A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks

Deployment of modern TinyML tasks on small battery-constrained IoT devic...

3 Angelo Garofalo, et al. ∙

research

∙ 10/18/2021

Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode

The Internet-of-Things requires end-nodes with ultra-low-power always-on...

25 Davide Rossi, et al. ∙

research

∙ 09/05/2021

A Fully-Integrated 5mW, 0.8Gbps Energy-Efficient Chip-to-Chip Data Link for Ultra-Low-Power IoT End-Nodes in 65-nm CMOS

The increasing complexity of Internet-of-Things (IoT) applications and n...

0 Hayate Okuhara, et al. ∙

research

∙ 09/03/2021

End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?

In-Memory Acceleration (IMA) promises major efficiency improvements in d...

0 Gianmarco Ottavi, et al. ∙

research

∙ 11/29/2020

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...

0 Angelo Garofalo, et al. ∙

research

∙ 11/08/2020

Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors

The main design principles in computer architecture have recently shifte...

0 Sergi Abadal, et al. ∙

research

∙ 10/09/2020

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...

0 Hayate Okuhara, et al. ∙

research

∙ 10/08/2020

A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference

Low bit-width Quantized Neural Networks (QNNs) enable deployment of comp...

0 Gianmarco Ottavi, et al. ∙

research

∙ 08/27/2020

A transprecision floating-point cluster for efficient near-sensor data analytics

Recent applications in the domain of near-sensor computing require the a...

0 Fabio Montagna, et al. ∙

research

∙ 08/17/2020

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...

13 Alessio Burrello, et al. ∙

research

∙ 07/27/2020

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...

0 Alfio Di Mauro, et al. ∙

research

∙ 07/17/2020

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...

0 Alfio Di Mauro, et al. ∙

research

∙ 07/15/2020

Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices

The deployment of Quantized Neural Networks (QNN) on advanced microcontr...

0 Nazareno Bruschi, et al. ∙

research

∙ 07/15/2020

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...

0 Ahmed Elnaqib, et al. ∙

research

∙ 06/25/2020

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, ...

0 Pasquale Davide Schiavone, et al. ∙

research

∙ 04/14/2020

Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters

The steeply growing performance demands for highly power- and energy-con...

0 Florian Glaser, et al. ∙

research

∙ 08/29/2019

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-...

0 Angelo Garofalo, et al. ∙

research

∙ 03/05/2018

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision...

0 Renzo Andri, et al. ∙

research

∙ 03/05/2018

Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes

Deep neural networks have achieved impressive results in computer vision...

0 Renzo Andri, et al. ∙

research

∙ 12/04/2017

NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in ...

0 Paolo Meloni, et al. ∙

research

∙ 11/28/2017

A Transprecision Floating-Point Platform for Ultra-Low Power Computing

In modern low-power embedded platforms, floating-point (FP) operations e...

0 Giuseppe Tagliavini, et al. ∙

research

∙ 04/29/2017

A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual ...

0 Manuele Rusci, et al. ∙

research

∙ 01/23/2017

Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes

High-performance computing systems are moving towards 2.5D and 3D memory...

0 Erfan Azarkhish, et al. ∙

research

∙ 12/18/2016

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...

0 Francesco Conti, et al. ∙

research

∙ 08/30/2016

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...

0 Michael Gautschi, et al. ∙

research

∙ 06/17/2016

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

Convolutional neural networks (CNNs) have revolutionized the world of co...

0 Renzo Andri, et al. ∙

Davide Rossi

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