Dataflow Systolic Array Implementations of Exploring Dual-Triangular Structure in QR Decomposition Using High-Level Synthesis

Siyang Jiang, Hsi-Wen Chen, Ming-Syan Chen
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引用次数: 2

Abstract

Tall and skinny QR (TSQR) decomposition is an essential matrix operation with various applications in edge computing, including data compression, subspace projection, and dimension reduction. As a critical component in TSQR, Dual-Triangular QR (DTQR) decomposition is solved by the Normal QR method in most works without utilizing the dual-triangular structure. Therefore, we propose a novel DTQR accelerator by recursively exploring the DT structure and propose three acceleration strategies with the systolic array to achieve higher parallelism. Experimental results manifest that our algorithm achieves 21.55x on average speedup compared with the baselines.
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基于高级综合的数据流收缩阵列在QR分解中探索双三角结构
TSQR分解是一种重要的矩阵运算,在边缘计算中有着广泛的应用,包括数据压缩、子空间投影和降维等。双三角QR (dual- triangle QR, DTQR)分解是TSQR的关键组成部分,在大多数工作中,没有使用双三角结构,而是采用Normal QR方法求解。因此,我们提出了一种新的DTQR加速器,通过递归探索DT结构,并提出了三种具有收缩阵列的加速策略,以实现更高的并行性。实验结果表明,与基线相比,我们的算法实现了21.55倍的平均加速。
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