571 citations · 2.8k across the 60 of their papers we have counts for
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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…
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization
Sean C. Smithson, Guang Yang, Warren J. Gross +1
Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition,…
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
Arash Ardakani, Carlo Condo, Warren J. Gross
Recently deep neural networks have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. Deep neural networks such as…
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…
Hardware Decoders for Polar Codes: An Overview
Pascal Giard, Gabi Sarkis, Alexios Balatsoukas-Stimming +5
Polar codes are an exciting new class of error correcting codes that achieve the symmetric capacity of memoryless channels. Many decoding algorithms were developed and implemented,…
Fast Low-Complexity Decoders for Low-Rate Polar Codes
Pascal Giard, Alexios Balatsoukas-Stimming, Gabi Sarkis +2
Polar codes are capacity-achieving error-correcting codes with an explicit construction that can be decoded with low-complexity algorithms. In this work, we show how the state-of-t…