关键词: 卷积神经网络（CNN）, 加速器, 并行计算, 设计空间探索, 乒乓缓存, 数据重用

Abstract:

In order to meet the requirements of low latency, small size and high throughput for Convolutional Neural Network（CNN） inference in practical applications, an accelerator is designed that uses the following optimization methods：for the storage access bandwidth limitation, the loop tiling factor is determined based on the design space exploration to improve the degree of data reuse; for the high computation density of CNN, it uses the loop unrolling technology to fully exploit the four kinds of computing parallelism; technologies such as memory pool, ping-pong cache, and dynamic data quantization are used to manage on-chip and off-chip storage resources. In addition, the process of generating accelerators is packaged as a CNN acceleration framework. Finally, the generated accelerator is used to implement the AlexNet network, the simulation results show that maximum computing throughput of this design is 1,493.4?Gops, which is up to 24.2 times of compared works, DSP efficiency exceeds other design methods, the lowest is 1.2 times. This paper achieves the rapid deployment of CNN, high development efficiency, and excellent acceleration performance.

Key words: Convolutional Neural Network（CNN）, accelerator, computing parallelism, design space exploration, ping pong cache, data reuse