Francesco Conti

research

∙ 09/07/2023

Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal Fluid via Topological Machine Learning

The cerebrospinal fluid (CSF) of 19 subjects who received a clinical dia...

0 Francesco Conti, et al. ∙

research

∙ 07/06/2023

Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge

Mixed-precision quantization, where a deep neural network's layers are q...

0 Georg Rutishauser, et al. ∙

research

∙ 07/03/2023

A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks

The emerging trend of deploying complex algorithms, such as Deep Neural ...

0 Alessandro Nadalini, et al. ∙

research

∙ 06/27/2023

A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms

Recent trends in deep learning (DL) imposed hardware accelerators as the...

0 Cristina Silvano, et al. ∙

research

∙ 05/30/2023

Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers

Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller U...

0 Davide Nadalini, et al. ∙

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

∙ 01/24/2023

Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge

Neural Architecture Search (NAS) is quickly becoming the go-to approach ...

0 Matteo Risso, 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

∙ 11/23/2022

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

∙ 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

∙ 04/22/2022

SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions

Event-based sensors are drawing increasing attention due to their high t...

0 Alfio Di Mauro, et al. ∙

research

∙ 03/28/2022

Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks

Temporal Convolutional Networks (TCNs) are promising Deep Learning model...

19 Matteo Risso, et al. ∙

research

∙ 03/24/2022

TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Lea...

3 Alessio Burrello, et al. ∙

research

∙ 02/14/2022

Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference

Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn...

5 Gianna Paulin, 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

∙ 10/20/2021

A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays

In the last few years, research and development on Deep Learning models ...

0 Leonardo Ravaglia, 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

∙ 09/03/2021

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

∙ 03/19/2021

Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs

Artificial intelligence-powered pocket-sized air robots have the potenti...

0 Daniele Palossi, 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

∙ 10/08/2020

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

∙ 08/17/2020

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

∙ 07/22/2020

Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node

AI-powered edge devices currently lack the ability to adapt their embedd...

0 Leonardo Ravaglia, 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

∙ 04/13/2020

Technical Report: NEMO DNN Quantization for Deployment Model

This technical report aims at defining a formal framework for Deep Neura...

7 Francesco Conti, 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

∙ 05/10/2019

An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs

Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diame...

0 Daniele Palossi, et al. ∙

research

∙ 02/04/2019

Optimally Scheduling CNN Convolutions for Efficient Memory Access

Embedded inference engines for convolutional networks must be parsimonio...

0 Arthur Stoutchinin, et al. ∙

research

∙ 07/09/2018

XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

Binary Neural Networks (BNNs) are promising to deliver accuracy comparab...

10 Francesco Conti, et al. ∙

research

∙ 05/04/2018

Ultra Low Power Deep-Learning-powered Autonomous Nano Drones

Flying in dynamic, urban, highly-populated environments represents an op...

0 Daniele Palossi, 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

∙ 11/15/2017

Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference

Recurrent neural networks (RNNs) are state-of-the-art in voice awareness...

0 Francesco Conti, 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. ∙

Francesco Conti

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