Co-Training of Feature Extraction and Classification using Partitioned Convolutional Neural Networks

Wei Yu Tsai, Jinhang Choi, Tulika Parija, Priyanka Gomatam, Chita Das, John Sampson, Vijaykrishnan Narayanan

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

3 Scopus citations


There are an increasing number of neuromorphic hardware platforms designed to efficiently support neural network inference tasks. However, many applications contain structured processing in addition to classification. Being able to map both neural network classification and structured computation onto the same platform is appealing from a system design perspective. In this paper, we perform a case study on mapping the feature extraction stage of pedestrian detection using Histogram of Oriented Gradients (HoG) onto a neuromophic platform. We consider three implementations: one that approximates HoG using neuromorphic intrinsics, one that emulates HoG outputs using a trained network, and one that allows feature extraction to be absorbed into classification. The proposed feature extraction methods are implemented and evaluated on neuromorphic hardware (IBM Neurosynaptic System). Our study shows that both a designed approximation and a "parroted" emulation can achieve similar accuracy, and that the latter appears to better capitalize on limited training and resource budgets, compared to the absorbed approach, while also being more power efficient than the programmed approach by a factor of 6.5x-208x.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450349277
StatePublished - Jun 18 2017
Event54th Annual Design Automation Conference, DAC 2017 - Austin, United States
Duration: Jun 18 2017Jun 22 2017

Publication series

NameProceedings - Design Automation Conference
VolumePart 128280
ISSN (Print)0738-100X


Other54th Annual Design Automation Conference, DAC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


Dive into the research topics of 'Co-Training of Feature Extraction and Classification using Partitioned Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this