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Erschienen in:
Improving Performance Estimation for FPGA-Based Accelerators for Convolutional Neural Networks Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

A Modular Software Library for Effective High Level Synthesis of Convolutional Neural Networks

verfasst von : Hector Gerardo Munoz Hernandez, Safdar Mahmood, Marcelo Brandalero, Michael Hübner

Erschienen in: Applied Reconfigurable Computing. Architectures, Tools, and Applications

Verlag: Springer International Publishing

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Abstract

Convolutional Neural Networks (CNNs) have applications in many valuable domains such as object detection for autonomous cars and security using facial recognition. This vast field of application usually places strict non-functional requirements such as resource-efficient implementations on the hardware devices, while at the same time requiring flexibility. In response, this work presents a C++-based software library of reusable modules to build arbitrary CNNs that support High-Level-Synthesis to be implemented as FPGA hardware accelerators for the inference process. Our work demonstrates how parametrization and modularization of basic building blocks of a CNN enable easier customization of the hardware to match the software model. This project also works with low-precision parameters throughout the CNN to provide a more resource-efficient implementation.

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Vorheriges Kapitel RISC-V Based MPSoC Design Exploration for FPGAs: Area, Power and Performance
Nächstes Kapitel HLS-Based Acceleration Framework for Deep Convolutional Neural Networks
Fußnoten
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Metadaten
Titel
A Modular Software Library for Effective High Level Synthesis of Convolutional Neural Networks
verfasst von
Hector Gerardo Munoz Hernandez
Safdar Mahmood
Marcelo Brandalero
Michael Hübner
Copyright-Jahr
2020
Buch
Applied Reconfigurable Computing. Architectures, Tools, and Applications

Print ISBN: 978-3-030-44533-1

Electronic ISBN: 978-3-030-44534-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-44534-8_16

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