Deep Learning on High Performance FPGA Switching Boards: Flow-in-Cloud

• Published in: Applied Reconfigurable Computing. Architectures, Tools, and Applications Vol. 10824; pp. 43 - 54
• Main Authors: Musha, Kazusa, Kudoh, Tomohiro, Amano, Hideharu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3319788892; 9783319788890
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-78890-6_4

FiC (Flow-in-Cloud)-SW is an FPGA-based switching node for an efficient AI computing system. It is equipped with a number of serial links directly connected to other nodes. Unlike other multi-FPGA systems, the circuit switching fabric with the STDM (Static Time Division Multiplexing) is implemented on the FPGA for predictable communication and cost-efficient data broadcasting. Parallel convolution modules for AlexNet are implemented on FiC-SW1 prototype boards consisting of Kintex Ultrascale FPGA, and evaluation results show that the parallel execution with 20 boards achieved 4.6 times better performance than the state of art implementation on a single Virtex 7 FPGA board.