MixedNet: Network Design Strategies for Cost-Effective Quantized CNNs

Abstract

This paper proposes design strategies for a low-cost quantized neural network. To prevent the classification accuracy from being degraded by quantization, a structure-design strategy that utilizes a large number of channels rather than deep layers is proposed. In addition, a squeeze-and-excitation (SE) layer is adopted to enhance the performance of the quantized network. Through a quantitative analysis and simulations of the quantized key convolution layers of ResNet and MobileNets, a low-cost layer-design strategy for use when building a neural network is proposed. With this strategy, a low-cost network referred to as a MixedNet is constructed. A 4-bit quantized MixedNet example achieves an on-chip memory size reduction of 60% and fewer memory access by 53% with negligible classification accuracy degradation in comparison with conventional networks while also showing classification accuracy rates of approximately 73% for Cifar-100 and 93% for Cifar-10.

Keywords

• Convolutional neural network
• deep neural network
• memory access number
• memory cost
• on-chip memory size
• quantized neural networks