27 citations · 27 across the 3 of their papers we have counts for
6 papers
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization
Manuele Rusci, Marco Fariselli, Martin Croome +2
This paper presents an optimized methodology to design and deploy Speech Enhancement (SE) algorithms based on Recurrent Neural Networks (RNNs) on a state-of-the-art MicroController…
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…
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
Angelo Garofalo, Manuele Rusci, Francesco Conti +2
We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels fo…
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…
Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?
Manuele Rusci, Lukas Cavigelli, Luca Benini
Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper pres…