24 citations · 26 across the 6 of their papers we have counts for
7 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…
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Angelo Garofalo, Gianmarco Ottavi, Francesco Conti +4
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory…
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…
A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference
Gianmarco Ottavi, Angelo Garofalo, Giuseppe Tagliavini +3
Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) by reducing their memory…
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
Alessio Burrello, Angelo Garofalo, Nazareno Bruschi +3
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applicati…
Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices
Nazareno Bruschi, Angelo Garofalo, Francesco Conti +2
The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instructi…