In this work, we propose to implement convolutional layers in the FD using the Hartley transformation (HT) instead of the Fourier transformation. We show that ...
Stochastic mean circuits (SMCs) are at the core of mean filtering and neural networks, but they have not been fully investigated in stochastic computing (SC).
ArticlePDF Available. Implementing Convolutional Neural Networks Using Hartley Stochastic Computing With Adaptive Rate Feature Map Compression. January 2021 ...
This work proposes to implement convolutional layers in the FD using the Hartley transform (HT) instead of the Fourier transformation, and shows that the HT ...
Hartley Stochastic Computing For Convolutional Neural Networks. In IEEE Workshop on Signal Processing Systems, SiPS 2021, Coimbra, Portugal, October 19-21 ...
Stochastic Computing Convolutional Neural Network Architecture ...
pmc.ncbi.nlm.nih.gov › PMC10911856
Mar 4, 2024 · SC uses simple logic gates to perform the arithmetic operation by exploiting probability mathematics to compute in the probability domain.
Missing: Hartley | Show results with:Hartley
This work proposes a novel architecture, called SkippyNN, that reduces the computation time of SC-based multiplications in the convolutional layers of CNNs, ...
This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then ...
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This paper presents a fully parallel and scalable hardware-based DCNN design using. Stochastic Computing (SC), which leverages the energy-accuracy trade-off ...
Missing: Hartley | Show results with:Hartley
Stochastic computing in convolutional neural network implementation
pmc.ncbi.nlm.nih.gov › PMC7924419
Nov 9, 2020 · This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure.
Missing: Hartley | Show results with:Hartley