一种高效的基于fpga的卷积加速器设计

Pengfei Song, Jeng-Shyang Pan, Chun-Sheng Yang, Chiou-Yng Lee
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引用次数: 0

摘要

采用模算术运算的数论变换可以有效地完成环内的卷积,且没有舍入误差。本文提出了一种支持不同操作数大小的高效转换结构。为了在面积和延迟之间取得平衡,使用了一种可变的常量几何结构,其中向前和向后子阶段使用相同的计算模式。此外,采用了基于xor的多端口RAM来加速内存访问,从而有效地实现了多个同时读写。因此,与其他设计相比,所开发的加速器可以实现更低的区域延迟FPGA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient FPGA-based accelerator design for convolution
Number theoretic transform with the modular arithmetic operations can perform convolution efficiently in a ring without round-off errors. In this paper, a new efficient architecture of the transform have been proposed which support a various operand size. To have a balanced trade-off between area and latency, a variant constant geometry architecture is used which the forward and backward sub-stage used the same computation pattern. In addition, a XOR-based multi-ported RAM is adopted to accelerate the memory access which allow multiple simultaneous reads and writes efficiently. As a result, the developed accelerator can achieve lower area-latency FPGA compared to other designs.
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