深度全连接网络中具有激活平稳数据流的收缩阵列

Haochuan Wan, Chaolin Rao, Yueyang Zheng, Pingqiang Zhou, Xin Lou
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引用次数: 0

摘要

提出了一种适用于纯全连接(FC)层网络的激活平稳(AS)数据流。结果表明,所提出的AS数据流有助于减少硬件设计中所需的内存大小,并通过减少数据移动来优化能源效率。基于AS数据流,提出了一种用于FC网络计算的输出平稳(OS)收缩阵列。为了评估所提出的设计,我们进一步实现了基于fc的MRI隐式表示(IREM)算法的加速器。开发了一种基于现场可编程门阵列(FPGA)的概念验证演示系统。为了评估提出的设计,我们还将IREM加速器映射到40nm CMOS技术,并将其与CPU, gpu和ASIC实现进行比较。
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A Systolic Array with Activation Stationary Dataflow for Deep Fully-Connected Networks
This paper presents an activation stationary (AS) dataflow suitable for networks with pure fully-connected (FC) layers. It is shown that the proposed AS dataflow can help to reduce the required memory size in hardware design and optimize energy efficiency by reducing data movement. Based on the AS dataflow, an output stationary (OS) systolic array is proposed to compute FC networks. To evaluate the proposed design, we further implement an accelerator for the FC-based implicit representation for MRI (IREM) algorithm. A proofof-concept demonstration system is developed based on field programmable gate array (FPGA). To evaluate the proposed design, We also map the IREM accelerator to 40nm CMOS technology and compare it with CPU, GPU-based and ASIC implementations.
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