260 citations · 456 across the 15 of their papers we have counts for
18 papers
Sequential Automorphism Ensemble Decoding with Early Stopping
Charles Pillet, Pascal Giard, Bassant Selim +1
In this paper, a low-complexity approach for the automorphism ensemble decoder (AED) using successive cancellation (SC) as constituent decoders is proposed. The approach sequential…
Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference
Théo Dupuis, Yoan Fournier, MohammadHossein AskariHemmat +4
Convolutional Neural Networks (CNNs) are used in a wide range of applications, with full-precision CNNs achieving high accuracy at the expense of portability. Recent progress in qu…
Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference
MohammadHossein AskariHemmat, Theo Dupuis, Yoan Fournier +8
In this paper, we present Quark, an integer RISC-V vector processor specifically tailored for sub-byte DNN inference. Quark is implemented in GlobalFoundries' 22FDX FD-SOI technolo…
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…
Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering
Jonathan Kern, Elsa Dupraz, Abdeldjalil Aïssa-El-Bey +2
This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the comp…
Energy Optimization of Faulty Quantized Min-Sum LDPC Decoders
Mohamed Yaoumi, Jeremy Nadal, Elsa Dupraz +2
The objective of this paper is to minimize the energy consumption of a quantized Min-Sum LDPC decoder, by considering aggressive voltage downscaling of the decoder circuit. Since l…
Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon +1
Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming so…
Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon +1
Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the…
Noisy Density Evolution With Asymmetric Deviation Models
Elsa Dupraz, François Leduc-Primeau
This paper considers low-density parity-check (LDPC) decoders affected by deviations introduced by the electronic device on which the decoder is implemented. Noisy density evolutio…
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hamed Hojatian, Vu Nguyen Ha, Jérémy Nadal +2
Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) sy…
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…
Training Modern Deep Neural Networks for Memory-Fault Robustness
Ghouthi Boukli Hacene, François Leduc-Primeau, Amal Ben Soussia +2
Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we inve…
A Study of Deep Learning Robustness Against Computation Failures
Jean-Charles Vialatte, François Leduc-Primeau
For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency. We study the performance of faulty implementations o…
Stall Pattern Avoidance in Polynomial Product Codes
Carlo Condo, Francois Leduc-Primeau, Gabi Sarkis +2
Product codes are a concatenated error-correction scheme that has been often considered for applications requiring very low bit-error rates, which demand that the error floor be de…
A 9.52 dB NCG FEC scheme and 164 bits/cycle low-complexity product decoder architecture
Carlo Condo, Pascal Giard, François Leduc-Primeau +2
Powerful Forward Error Correction (FEC) schemes are used in optical communications to achieve bit-error rates below . These FECs follow one of two approaches: concatenati…
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
Arash Ardakani, François Leduc-Primeau, Naoya Onizawa +2
The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware…
Modeling and Energy Optimization of LDPC Decoder Circuits with Timing Violations
François Leduc-Primeau, Frank R. Kschischang, Warren J. Gross
This paper proposes a "quasi-synchronous" design approach for signal processing circuits, in which timing violations are permitted, but without the need for a hardware compensation…
Relaxed Half-Stochastic Belief Propagation
François Leduc-Primeau, Saied Hemati, Shie Mannor +1
Low-density parity-check codes are attractive for high throughput applications because of their low decoding complexity per bit, but also because all the codeword bits can be decod…