A layer-block-wise pipeline for memory and bandwidth reduction in distributed deep learning

Abstract

This paper describes a pipelined stochastic gradient descent (SGD) algorithm and its hardware architecture with a memory distributed structure. In the proposed architecture, a pipeline stage takes charge of multiple layers: a “layer block.” The layer-block-wise pipeline has much less weight parameters for network training than conventional multithreading because weight memory is distributed to workers assigned to pipeline stages. The memory capacity of 2.25 GB for the four-stage proposed pipeline is about half of the 3.82 GB for multithreading when a batch size is 32 in VGG-F. Unlike multithreaded data parallelism, no parameter server for weight update or shared I/O data bus is necessary. Therefore, the memory bandwidth is drastically reduced. The proposed four-stage pipeline only needs memory bandwidths of 36.3 MB and 17.0 MB per batch, respectively, for forward propagation and backpropagation processes, whereas four-thread multithreading requires a bandwidth of 974 MB overall for send and receive processes to unify its weight parameters. At the parallelization degree of four, the proposed pipeline maintains training convergence by a factor of 1.12, compared with the conventional multithreaded architecture although the memory capacity and the memory bandwidth are decreased.