428 citations · 4.5k across the 191 of their papers we have counts for
5 papers · 2 filters
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
Xiaying Wang, Michele Magno, Lukas Cavigelli +1
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine…
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
Florian Scheidegger, Luca Benini, Costas Bekas +1
Deep neural networks achieve outstanding results in challenging image classification tasks. However, the design of network topologies is a complex task and the research community m…
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
Martino Dazzi, Abu Sebastian, Pier Andrea Francese +3
In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency. The essential idea is…
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…
Additive Noise Annealing and Approximation Properties of Quantized Neural Networks
Matteo Spallanzani, Lukas Cavigelli, Gian Paolo Leonardi +2
We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural network…