title: CURSOR-BASED ADAPTIVE QUANTIZATION FOR DEEP NEURAL NETWORK
abstract: Deep neural network (DNN) has rapidly found many applications in different scenarios.
  However, its large computational cost and memory consumption are barriers
  to computing restrained applications. DNN model quantization is a widely
  used method to reduce the DNN storage and computation burden by decreasing
  the bit width. In this paper, we propose a novel cursor based adaptive quantization
  method using differentiable architecture search (DAS). The multiple bits’
  quantization mechanism is formulated as a DAS process with a continuous cursor
  that represents the possible quantization bit. The cursor-based DAS adaptively
  searches for the desired quantization bit for each layer. The DAS process can
  be solved via an alternative approximate optimization process, which is designed
  for mixed quantization scheme of a DNN model. We further devise a new loss
  function in the search process to simultaneously optimize accuracy and parameter
  size of the model. In the quantization step, based on a new strategy, the closest
  two integers to the cursor are adopted as the bits to quantize the DNN together to
  reduce the quantization noise and avoid the local convergence problem. Comprehensive
  experiments on benchmark datasets show that our cursor based adaptive
  quantization approach achieves the new state-of-the-art for multiple bits’ quantization
  and can efficiently obtain lower size model with comparable or even better
  classification accuracy.
authors: Bapu Li(*) Yanwen Fan(*) Zhiyu Cheng Yingze Bao (* means equal contribution)
hIndex: Yanwen Fan(*) 4