MuDBN

Abstract

With the increasing size of neural networks, state-of-the-art deep neural networks (DNNs) have hundreds of millions of parameters. Due to multiple fully-connected layers, DNNs are compute-intensive and memory-intensive, making them hard to deploy on embedded devices with limited power budgets and hardware resources. Therefore, this paper presents a deep belief network accelerator based on multi-FPGA. Two different schemes, the division between layers (DBL) and the division inside layers (DIL), are adopted to map the DBN to the multi-FPGA system. Experimental results demonstrate that the accelerator can achieve 4.24x (DBL) -6.20x (DIL) speedup comparing to the Intel Core i7 CPU and save 119x (DBL) -90x (DIL) power consumption comparing to the Tesla K40C GPU.