Reducing the model size and computation costs for dedicated AI accelerator designs, neural network quantization methods have attracted momentous attention recently. Unfortunately, merely minimizing quantization loss using constant discretization causes accuracy deterioration. In this paper, we propose an iterative accuracy-driven learning framework of competitive-collaborative quantization (CCQ) to gradually adapt the bit-precision of each individual layer. Orthogonal to prior quantization policies working with full precision for the first and last layers of the network, CCQ offers layer-wise competition for any target quantization policy with holistic layer fine-tuning to recover accuracy, where the state-of-the-art networks can be entirely quantized without any significant accuracy degradation.
|Title of host publication
|2020 57th ACM/IEEE Design Automation Conference, DAC 2020
|Institute of Electrical and Electronics Engineers Inc.
|Published - Jul 2020
|57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
Duration: Jul 20 2020 → Jul 24 2020
|Proceedings - Design Automation Conference
|57th ACM/IEEE Design Automation Conference, DAC 2020
|Virtual, San Francisco
|7/20/20 → 7/24/20
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modeling and Simulation