FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

27 Feb 2019Paul N. WhatmoughChuteng ZhouPatrick HansenShreyas Kolala VenkataramanaiahJae-sun SeoMatthew Mattina

The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed the energy budgets of mobile devices. This paper proposes FixyNN, which consists of a fixed-weight feature extractor that generates ubiquitous CNN features, and a conventional programmable CNN accelerator which processes a dataset-specific CNN... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.