Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large WeightsDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • TL;DR: A representation of quantized neural networks with both values and indices. Centralizing weights for the efficiency of the representation.
  • Abstract: Previous ternarizations such as the trained ternary quantization (TTQ), which quantized weights to three values (e.g., {−Wn, 0,+Wp}), achieved the small model size and efficient inference process. However, the extreme limit on the number of quantization steps causes some degradation in accuracy. To solve this problem, we propose a hybrid weight representation (HWR) method which produces a network consisting of two types of weights, i.e., ternary weights (TW) and sparse-large weights (SLW). The TW is similar to the TTQ’s and requires three states to be stored in memory with 2 bits. We utilize the one remaining state to indicate the SLW which is referred to as very rare and greater than TW. In HWR, we represent TW with values while SLW with indices of values. By encoding SLW, the networks can preserve their model size with improving their accuracy. To fully utilize HWR, we also introduce a centralized quantization (CQ) process with a weighted ridge (WR) regularizer. They aim to reduce the entropy of weight distributions by centralizing weights toward ternary values. Our comprehensive experiments show that HWR outperforms the state-of-the-art compressed models in terms of the trade-off between model size and accuracy. Our proposed representation increased the AlexNet performance on CIFAR-100 by 4.15% with only1.13% increase in model size.
  • Keywords: quantized neural networks, centralized quantization, hybrid weight representation, weighted ridge, ternary weight
7 Replies