title: Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
abstract: Recent work has shown that performing inference with fast, very-low-bitwidth
  (e.g., 1 to 2 bits) representations of values in models can yield surprisingly accurate
  results. However, although 2-bit approximated networks have been shown to
  be quite accurate, 1 bit approximations, which are twice as fast, have restrictively
  low accuracy. We propose a method to train models whose weights are a mixture
  of bitwidths, that allows us to more finely tune the accuracy/speed trade-off. We
  present the “middle-out” criterion for determining the bitwidth for each value, and
  show how to integrate it into training models with a desired mixture of bitwidths.
  We evaluate several architectures and binarization techniques on the ImageNet
  dataset. We show that our heterogeneous bitwidth approximation achieves superlinear
  scaling of accuracy with bitwidth. Using an average of only 1.4 bits, we are
  able to outperform state-of-the-art 2-bit architectures.
authors: Josh Fromm Matthai Philipose Shwetak Patel
hIndex: Shwetak Patel 52