While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. We modify a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism, and rigorously analyze its error. We prove that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization. Our result holds across a range of input distributions and for both fully-connected and convolutional architectures. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.

本研究提出了一种针对神经网络量化的方法，该方法通过量化权重来降低计算成本、内存占用和功耗，并促进权重的稀疏性，使用本方法在ImageNet上测试显示准确性的损失很小。

具有可证明保证的神经网络后训练量化