Deep neural networks (DNNs) demand a very large amount of computation and
weight storage, and thus efficient implementation using special purpose
hardware is highly desired. In this work, we have developed an FPGA based
fixed-point DNN system using only on-chip memory not to access external DRAM.
The execution time and energy consumption of the developed system is compared
with a GPU based implementation. Since the capacity of memory in FPGA is
limited, only 3-bit weights are used for this implementation, and training
based fixed-point weight optimization is employed. The implementation using
Xilinx XC7Z045 is tested for the MNIST handwritten digit recognition benchmark
and a phoneme recognition task on TIMIT corpus. The obtained speed is about one
quarter of a GPU based implementation and much better than that of a PC based
one. The power consumption is less than 5 Watt at the full speed operation
resulting in much higher efficiency compared to GPU based systems.

本研究开发了一种基于 FPGA 的定点深度神经网络系统，实现了手写数字识别和音素识别任务，相较于基于 GPU 的系统有更高的效率和更低的功耗。