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Optimizing Bit-Serial Matrix Multiplication for Reconfigurable Computing

delete2019-08-20
delete12
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OA
AI
Y
Yaman Umuroglu *
D
Davide Conficconi
L
Lahiru Rasnayake
T
Thomas B. Preußer
M
Magnus Själander
DOI:10.1145/3337929delete
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Abstract

Abstract

En 中文
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication-dependent applications can use reduced-precision integer or fixed-point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. BISMO, a vectorized bit-serial matrix multiplication overlay for reconfigurable computing, previously utilized the excellent binary-operation performance of FPGAs to offer a matrix multiplication performance that scales with required precision and parallelism. We show how BISMO can be scaled up on Xilinx FPGAs using an arithmetic architecture that better utilizes six-input LUTs. The improved BISMO achieves a peak performance of 15.4 binary TOPS on the Ultra96 board with a Xilinx UltraScale+ MPSoC.
Keywords:
Bit serial
matrix multiplication
overlay
FPGA
AI Summary

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Journal

ACM Transactions on Reconfigurable Technology and Systems cover
ACM Transactions on Reconfigurable Technology and Systems
IF:
2.8
Papers:
597
Citations:
810

Organization

X
xilinx
Scholars:
67
Papers: 46
Citations: 0
U
uppsala university
Scholars:
3.7W
Papers: 3.4W
Citations: 47