30 citations · 57 across the 3 of their papers we have counts for
4 papers
Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers
Manuele Rusci, Marco Fariselli, Alessandro Capotondi +1
The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if le…
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…
Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers
Manuele Rusci, Alessandro Capotondi, Luca Benini
This paper presents a novel end-to-end methodology for enabling the deployment of low-error deep networks on microcontrollers. To fit the memory and computational limitations of re…
HERO: Heterogeneous Embedded Research Platform for Exploring RISC-V Manycore Accelerators on FPGA
Andreas Kurth, Pirmin Vogel, Alessandro Capotondi +2
Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host processor with programmable manycore accelerators (PMCAs) to combine general-purpose computing with dom…