activity
20172026
most citedPULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors

136 citations · 266 across the 18 of their papers we have counts for

collaborators
Showing cs.DCShow all

1 paper · 1 filter

cs.DC2020

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

Leonardo Ravaglia, Manuele Rusci, Alessandro Capotondi +5

AI-powered edge devices currently lack the ability to adapt their embedded inference models to the ever-changing environment. To tackle this issue, Continual Learning (CL) strategi…