CASCADE: A Framework for CNN Accelerator Synthesis With Concatenation and Refreshing Dataflow

Abstract

Layer Pipeline (LP) represents an innovative architecture for neural network accelerators, which implements task-level pipelining at the granularity of layers. Despite improvements in throughput, LP architectures face challenges due to complicated dataflow design, intricate design space and high resource requirements. In this paper, we introduce an accelerator synthesis framework, CASCADE. CASCADE leverages a novel dataflow, CARD, to efficiently manage convolutional operations’ irregular memory access patterns using simplified logic and minimal buffers. It also employs advanced design space exploration methods to optimize unrolling parallelism and FIFO depth settings automatically for each layer. Finally, to further enhance resource efficiency, CASCADE leverages Lookup Table-based multiplication and accumulation units. With extensive experimental results, we demonstrate that CASCADE significantly outperforms existing works, achieving a $3\times $ improvement in resource efficiency and a $4\times $ improvement in power efficiency. It achieves over $1.5\times 10^{4}$ frames per second throughput and 71.9% accuracy on ImageNet.