Multi-precision convolutional neural networks on heterogeneous hardware

Sam Amiri, Mohammad Hosseinabady, Simon McIntosh-Smith, Jose Nunez-Yanez

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

7 Citations (Scopus)
39 Downloads (Pure)


Fully binarised convolutional neural networks (CNNs) deliver very high inference performance using single-bit weights and activations, together with XNOR type operators for the kernel convolutions. Current research shows that full binarisation results in a degradation of accuracy and different approaches to tackle this issue are being investigated such as using more complex models as accuracy reduces. This paper proposes an alternative based on a multi-precision CNN frame-work that combines a binarised and a floating point CNN in a pipeline configuration deployed on heterogeneous hardware. The binarised CNN is mapped onto an FPGA device and used to perform inference over the whole input set while the floating point network is mapped onto a CPU device and performs re-inference only when the classification confidence level is low. A light-weight confidence mechanism enables a flexible trade-off between accuracy and throughput. To demonstrate the concept, we choose a Zynq 7020 device as the hardware target and show that the multi-precision network is able to increase the BNN accuracy from 78.5% to 82.5% and the CPU inference speed from 29.68 to 90.82 images/sec.
Original languageEnglish
Title of host publicationDesign, Automation & Test in Europe Conference & Exhibition (DATE)
Number of pages6
ISBN (Electronic)978-3-9819263-0-9
ISBN (Print)978-3-9819263-1-6
Publication statusPublished - Mar 2018
Event DATE - Design, Automation and Test in Europe Conference - Dresden, Germany
Duration: 19 Mar 201823 Mar 2018

Publication series

NameDate Proceedings
ISSN (Electronic)1558-1101


Conference DATE - Design, Automation and Test in Europe Conference
Abbreviated titleDATE 2018

Bibliographical note

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


  • multi-precision
  • performance
  • Convolutional Neural Network
  • deep learning
  • heterogeneous
  • FPGA
  • ARM
  • CIFAR-10
  • inference


Dive into the research topics of 'Multi-precision convolutional neural networks on heterogeneous hardware'. Together they form a unique fingerprint.

Cite this