Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources. However, existing quantization methods often represent all weights and activations with the same precision (bit-width). In this paper, we explore a new dimension of the design space: quantizing different layers with different bit-widths. We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search (DNAS) framework to efficiently explore its exponential search space with gradient-based optimization. Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet. Our quantized models with 21.1x smaller model size or 103.9x lower computational cost can still outperform baseline quantized or even full precision models.
A novel differentiable neural architecture search framework for mixed quantization of ConvNets. The authors introduce a new method for neural architecture search which selects the precision quantization of weights at each neural network layer, and use it in the context of network compression. The paper presents a new approach in network quantization by quantizing different layers with different bit-widths and introduces a new differentiable neural architecture search framework.