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

  • Hector Gerardo Munoz HernandezEmail author
  • Safdar Mahmood
  • Marcelo Brandalero
  • Michael Hübner
Conference paper
  • 12 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12083)

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.

Keywords

High Level Synthesis Modular approach HW acceleration Convolutional Neural Networks Inference acceleration Library of components Machine learning C library FPGA 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Brandenburg University of Technology Cottbus - Senftenberg Computer Engineering GroupCottbusGermany

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