Parameterizable Design on Convolutional Neural Networks Using Chisel Hardware Construction Language

This paper presents a parameterizable design generator on convolutional neural networks (CNNs) using the Chisel hardware construction language (HCL). By parameterizing structural designs such as the streaming width, pooling layer type, and floating point precision, multiple register–transfer level (...

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Detalles Bibliográficos
Autores principales: Madineni, Mukesh Chowdary, Vega, Mario, Yang, Xiaokun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053615/
https://www.ncbi.nlm.nih.gov/pubmed/36984938
http://dx.doi.org/10.3390/mi14030531

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053615/
https://www.ncbi.nlm.nih.gov/pubmed/36984938
http://dx.doi.org/10.3390/mi14030531

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