fpga上无补零稀疏矩阵向量乘法

Jiasen Huang, Junyan Ren, Wenbo Yin, Lingli Wang
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引用次数: 1

摘要

稀疏矩阵向量乘法(SpMxV)算法由于不规律的内存访问而遭受严重的性能损失。在本文中,我们引入了一种新的压缩元素存储(CES)格式,其中放弃了用于索引的额外数据结构,并且与矩阵的非零元素相关的每个位置现在由变量的名称乘以向量的相应元素来表示。为了确保最快的访问和并行访问而没有数据危害,片上寄存器专门用于取代BRAM或片外DRAM/SRAM来保存所有SpMxV数据。充分利用片上DSP资源,保证最大数量的乘数同时工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
No zero padded sparse matrix-vector multiplication on FPGAs
Sparse Matrix-Vector Multiplication (SpMxV) algorithms suffer heavy performance penalties due to irregular memory accesses. In this paper, we introduce a novel compressed element storage (CES) format, in which the additional data structures for indexing are abandoned, and each location associated with the non-zero element of the matrix is now indicated by the name of a variable multiplied by the corresponding element of the vector. To ensure fastest access and parallel access without data hazards, on-chip registers are used exclusively to replace the BRAM or off-chip DRAM/SRAM to hold all the SpMxV data. On-chip DSP resources are fully utilized so as to ensure a maximum number of multipliers concurrently working.
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