13 citations · 27 across the 5 of their papers we have counts for
8 papers
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Jonathan Kern, Sébastien Henwood, Gonçalo Mordido +4
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural ne…
Bridging the Gap: FPGAs as Programmable Switches
Thomas Luinaud, Thibaut Stimpfling, Jeferson Santiago da Silva +2
The emergence of P4, a domain specific language, coupled to PISA, a domain specific architecture, is revolutionizing the networking field. P4 allows to describe how packets are pro…
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA
Alireza Ghaffari, Yvon Savaria
Convolutional Neural Networks (CNNs) have a major impact on our society because of the numerous services they provide. On the other hand, they require considerable computing power.…
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks
Sébastien Henwood, François Leduc-Primeau, Yvon Savaria
Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference. This paper considers the…
U-Net Fixed-Point Quantization for Medical Image Segmentation
MohammadHossein AskariHemmat, Sina Honari, Lucas Rouhier +4
Model quantization is leveraged to reduce the memory consumption and the computation time of deep neural networks. This is achieved by representing weights and activations with a l…
SHIP: A Scalable High-performance IPv6 Lookup Algorithm that Exploits Prefix Characteristics
Thibaut Stimpfling, Normand Bélanger, J. M. Pierre Langlois +1
Due to the emergence of new network applications, current IP lookup engines must support high-bandwidth, low lookup latency and the ongoing growth of IPv6 networks. However, existi…