Compact Powers-of-Two: An Efficient Non-Uniform Quantization for Deep Neural Networks
Xinkuang Geng, Siting Liu, Jianfei Jiang, Kai Jiang, Honglan Jiang · 2024
To reduce the demands for computation and memory of deep neural networks (DNNs), various quantization techniques have been extensively investigated. However, conventional methods cannot effectively capture the intrinsic data characteristics in DNNs, leading to a high accuracy degradation when employing low-bit-width quantization. In order to better align with the bell-shaped distribution, we propose an efficient non-uniform quantization scheme, denoted as compact powers-of-two (CPoT). Aiming to avoid the rigid resolution inherent in powers-of-two (PoT) without introducing new issues, we add a fractional part to its encoding, followed by a biasing operation to eliminate the unrepresentable region around O. This approach effectively balances the grid resolution in both the vicinity of 0 and the edge region. To facilitate the hardware implementation, we optimize the dot product for CPoT based on the computational characteristics of the quantized DNNs, where the precomputable terms are extracted and incorporated into bias. Consequently, a multiply-accumulate (MAC) unit is designed for CPoT using shifters and look-up tables (LUTs). The experimental results show that, even with a certain level of approximation, our proposed CPoT outperforms state-of-the-art methods in data-free quantization (DFQ), a post-training quantization (PTQ) technique focusing on data privacy and computational efficiency. Furthermore, CPoT demonstrates superior efficiency in area and power compared to other methods in hardware implementation.