1 citations · 1 across the 7 of their papers we have counts for
15 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…