Davide Rossi

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University of Bologna

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...
11/29/2020 ∙ by Angelo Garofalo, et al. ∙ 0 ∙ share

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

The main design principles in computer architecture have recently shifte...
11/08/2020 ∙ by Sergi Abadal, et al. ∙ 0 ∙ share

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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...
10/09/2020 ∙ by Hayate Okuhara, et al. ∙ 0 ∙ share

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

Low bit-width Quantized Neural Networks (QNNs) enable deployment of comp...
10/08/2020 ∙ by Gianmarco Ottavi, et al. ∙ 0 ∙ share

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

Recent applications in the domain of near-sensor computing require the a...
08/27/2020 ∙ by Fabio Montagna, et al. ∙ 0 ∙ share

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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...
08/17/2020 ∙ by Alessio Burrello, et al. ∙ 13 ∙ share

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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...
07/27/2020 ∙ by Alfio Di Mauro, et al. ∙ 0 ∙ share

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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...
07/17/2020 ∙ by Alfio Di Mauro, et al. ∙ 0 ∙ share

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

The deployment of Quantized Neural Networks (QNN) on advanced microcontr...
07/15/2020 ∙ by Nazareno Bruschi, et al. ∙ 0 ∙ share

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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...
07/15/2020 ∙ by Ahmed Elnaqib, et al. ∙ 0 ∙ share

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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, ...
06/25/2020 ∙ by Pasquale Davide Schiavone, et al. ∙ 0 ∙ share

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

The steeply growing performance demands for highly power- and energy-con...
04/14/2020 ∙ by Florian Glaser, et al. ∙ 0 ∙ share

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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-...
08/29/2019 ∙ by Angelo Garofalo, et al. ∙ 0 ∙ share

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

Deep neural networks have achieved impressive results in computer vision...
03/05/2018 ∙ by Renzo Andri, et al. ∙ 0 ∙ share

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

Deep neural networks have achieved impressive results in computer vision...
03/05/2018 ∙ by Renzo Andri, et al. ∙ 0 ∙ share

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

Deep convolutional neural networks (CNNs) obtain outstanding results in ...
12/04/2017 ∙ by Paolo Meloni, et al. ∙ 0 ∙ share

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

In modern low-power embedded platforms, floating-point (FP) operations e...
11/28/2017 ∙ by Giuseppe Tagliavini, et al. ∙ 0 ∙ share

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

This work presents a fully-programmable Internet of Things (IoT) visual ...
04/29/2017 ∙ by Manuele Rusci, et al. ∙ 0 ∙ share

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

High-performance computing systems are moving towards 2.5D and 3D memory...
01/23/2017 ∙ by Erfan Azarkhish, et al. ∙ 0 ∙ share

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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...
12/18/2016 ∙ by Francesco Conti, et al. ∙ 0 ∙ share

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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...
08/30/2016 ∙ by Michael Gautschi, et al. ∙ 0 ∙ share

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

Convolutional neural networks (CNNs) have revolutionized the world of co...
06/17/2016 ∙ by Renzo Andri, et al. ∙ 0 ∙ share

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Davide Rossi

Featured Co-authors

Luca Benini

Lukas Cavigelli

Francesco Conti

Sergi Abadal

Abu Sebastian

Giuseppe Tagliavini

Stefan Mangard

Manuele Rusci

Akshay Jain

Renzo Andri

Geethan Karunaratne

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes

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

An Energy-Efficient Low-Voltage Swing Transceiver for mW-Range IoT End-Nodes

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

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

DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

Performance-Aware Predictive-Model-Based On-Chip Body-Bias Regulation Strategy for an ULP Multi-Core Cluster in 28nm UTBB FD-SOI

Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node

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

A 0.5GHz 0.35mW LDO-Powered Constant-Slope Phase Interpolator with 0.22% INL

Arnold: an eFPGA-Augmented RISC-V SoC for Flexible and Low-Power IoT End-Nodes

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

PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

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

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

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

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

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

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

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices

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