110 citations · 180 across the 9 of their papers we have counts for
12 papers
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
Nazareno Bruschi, Giuseppe Tagliavini, Angelo Garofalo +4
The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memo…
GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors
Nazareno Bruschi, Germain Haugou, Giuseppe Tagliavini +3
The last few years have seen the emergence of IoT processors: ultra-low power systems-on-chips (SoCs) combining lightweight and flexible micro-controller units (MCUs), often based…
Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode
Davide Rossi, Francesco Conti, Manuel Eggimann +9
The Internet-of-Things requires end-nodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibi…
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang +5
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on t…
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers
Emanuele Parisi, Francesco Barchi, Andrea Bartolini +2
The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit moder…
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
Angelo Garofalo, Giuseppe Tagliavini, Francesco Conti +2
This work introduces lightweight extensions to the RISC-V ISA to boost the efficiency of heavily Quantized Neural Network (QNN) inference on microcontroller-class cores. By extendi…