Model quantization is an important mechanism for energy-efficient deployment of deep neural networks on resource-constrained devices by reducing the bit precision of weights and activations. However, it remains challenging to maintain high accuracy as bit precision decreases, especially for low-precision networks (e.g., 2-bit MobileNetV2). Existing methods have been explored to address this problem by minimizing the quantization error or mimicking the data distribution of full-precision networks. In this work, we propose a novel weight regularization algorithm for improving low-precision network quantization. Instead of constraining the overall data distribution, we separably optimize all elements in each quantization bin to be as close to the target quantized value as possible. Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally. Experiments demonstrate that our method achieves consistent improvements over the state-of-the-art quantization-aware training methods for different low-precision networks. Particularly, our bin regularization improves LSQ for 2-bit MobileNetV2 and MobileNetV3-Small by 3.9% and 4.9% top-1 accuracy on ImageNet, respectively.

Low bit quantization of neural network is required on edge devices to achieve lower power consumption and higher performance. 8bit or binary network either consumes a lot of resources or has accuracy degradation. Thus, a full-process hardware-friendly quantization solution of 4A4W (activations 4bit and weights 4bit) is proposed to achieve better accuracy/resource trade-off. It doesn't contain any additional floating operations and achieve accuracy comparable to full-precision. We also implement a low-precision accelerator for CNN (LPAC) on the Xilinx FPGA, which takes full advantage of its DSP by efficiently mapping convolutional computations. Through on-chip reassign management and resource-saving analysis, high performance can be achieved on small chips. Our 4A4W solution achieves 1.8x higher performance than 8A8W and 2.42x increase in power efficiency under the same resource. On ImageNet classification, the accuracy has a gap less than 1% to full-precision in Top-5. On the human pose estimation, we achieve 261 frames per second on ZU2EG, which is 1.78x speed up compared to 8A8W and the accuracy has only 1.62% gap to full-precision. This proves that our solution has better universality.