Machine learning on FPGAs to face the IoT revolution

Xiaofan Zhang, Anand Ramachandran, Chuanhao Zhuge, Di He, Wei Zuo, Zuofu Cheng, Kyle Rupnow, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


FPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with improved latency and energy efficiency compared to CPU and GPU-based implementations. High-level synthesis (HLS) is an effective design flow for DNNs due to improved productivity, debugging, and design space exploration ability. However, optimizing large neural networks under resource constraints for FPGAs is still a key challenge. In this paper, we present a series of effective design techniques for implementing DNNs on FPGAs with high performance and energy efficiency. These include the use of configurable DNN IPs, performance and resource modeling, resource allocation across DNN layers, and DNN reduction and re-training. We showcase several design solutions including Long-term Recurrent Convolution Network (LRCN) for video captioning, Inception module for FaceNet face recognition, as well as Long Short-Term Memory (LSTM) for sound recognition. These and other similar DNN solutions are ideal implementations to be deployed in vision or sound based IoT applications.

Original languageEnglish (US)
Title of host publication2017 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538630938
StatePublished - Dec 13 2017
Event36th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017 - Irvine, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Other36th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2017
Country/TerritoryUnited States


  • FPGAs
  • Internet of Things
  • Machine Learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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