Using Quantization-Aware Training Technique with Post-Training Fine-Tuning Quantization to Implement a MobileNet Hardware Accelerator

Abstract

In recent years, the internet of things (IoT) has been developed near the public's life circle. At the edge device, for real-time data analysis of data, a lightweight deep learning neural network (DNN) is required. In this paper, the lightweight model MobileNet is used to design an energy efficiency hardware accelerator at the edge device. In the software framework (Tensorflow), the quantization-aware training technique with post-training fine-tuning quantization is applied to quantize the model to improve training convergence speed and parameter minimization. In hardware design considerations, fixed-point operations can reduce computational complexity and memory storage space as compared to floating-point operations, which directly affects the power consumption of the circuit. The proposed MobileNet hardware accelerator can achieve low power consumption and is suitable for the edge devices.