In the post-Moore's law era, the quest for enhanced computational
power has led to exploration beyond traditional electrical digital
computing. Integrated Network Interface Cards (NICs) have emerged
as a key player in high-performance computing, offering low latency
and high bandwidth. To address throughput limitations in Systolic
array hardware, a reconfigured software-defined System-on-Chip
(SoC) utilizing Advanced Microcontroller Bus Architecture (AMBA)
standards is proposed. This study introduces a block data trimming
methodology that improves hybrid computing efficiency. The designed
Systolic array Matrix Multiply Unit (MMU) is tested with a maximum
size of 32 × 32 and 1,024 Multiply Accumulator (MAC) units. Hybrid
dynamic circuits are implemented to support int8, int16, int32, and
int64 data types, optimizing parallel computing performance. The new
AI accelerators exhibit a 2× increase in throughput and a 1.33×
improvement in DSP efficiency compared to the previous FireFly
version, and achieve 1.42× better power efficiency than the leading
FPGA accelerators.
S. Kaliswaran1, R. Saranya2, Ajeet Kumar Srivastava3, C. Saravanakumar4, Deepali Suhas Jadhav5
Government Arts and Science College, Perumbakkam, India1, V.S.B. Engineering College, India2, Chhatrapati Shahu Ji Maharaj University, India3, SRM Valliammai Engineering College, India4, Vishwakarma Institute of Technology, India5
FPGA, Systolic Array, AI Accelerators, High-Performance Computing, SoC
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| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 10 , Issue: 2 , Pages: 1784 - 1789 )
Date of Publication :
July 2024
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126
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