S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift NetworksDownload PDF

May 21, 2021 (edited Jan 24, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Convolution Neural Network, CNN, Edge Computing, Edge, Quantization, Low-bit, Multiplication-free, Bit-shift
  • TL;DR: S$^3$ re-parameterization for efficient training of low-bit shift networks combating vanishing gradient problem and weight sign freezing problem, 3-bit multiplication-free network compete full-precision model on ImageNet.
  • Abstract: Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast and energy-efficient compared to conventional neural networks. However, existing shift networks are sensitive to the weight initialization and yield a degraded performance caused by vanishing gradient and weight sign freezing problem. To address these issues, we propose S$^3$ re-parameterization, a novel technique for training low-bit shift networks. Our method decomposes a discrete parameter in a sign-sparse-shift 3-fold manner. This way, it efficiently learns a low-bit network with weight dynamics similar to full-precision networks and insensitive to weight initialization. Our proposed training method pushes the boundaries of shift neural networks and shows 3-bit shift networks compete with their full-precision counterparts in terms of top-1 accuracy on ImageNet.
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