Promoting the Harmony between Sparsity and Regularity: A Relaxed Synchronous Architecture for Convolutional Neural Networks

There are two approaches to improve the performance of Convolutional Neural Networks (CNNs): 1) accelerating computation and 2) reducing the amount of computation. The acceleration approaches take the advantage of CNN computing regularity which enables abundant fine-grained parallelisms in feature maps, neurons, and synapses. Alternatively, reducing computations leverages the intrinsic sparsity of CNN neurons and synapses. The sparsity represents as the computing bubbles, i.e., zero or tiny-valued neurons and synapses. These bubbles can be removed to reduce the volume of computations. Although distinctly different from each other in principle, we find that the two types of approaches are not orthogonal to each other. Even worse, they may conflict to each other when working together. The conditional branches introduced by some bubble-removing mechanisms in the original computations destroy the regularity of deeply nested loops, thereby impairing the intrinsic parallelisms. Therefore, enabling the synergy between the two types of approaches is critical to arrive at superior performance. This paper proposed a relaxed synchronous computing architecture, FlexFlow-Pro, to fulfill this purpose. Compared with the state-of-the-art accelerators, the FlexFlow-Pro gains more than 2.5× performance on average and 2× energy efficiency.