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

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.