Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165562
Title: MUGNoC: a software-configured multicast-unicast-gather NoC for accelerating CNN dataflows
Authors: Chen, Hui
Liu, Di
Li, Shiqing
Huai, Shuo
Luo, Xiangzhong
Liu, Weichen
Keywords: Engineering::Computer science and engineering::Hardware
Issue Date: 2023
Source: Chen, H., Liu, D., Li, S., Huai, S., Luo, X. & Liu, W. (2023). MUGNoC: a software-configured multicast-unicast-gather NoC for accelerating CNN dataflows. 28th Asia and South Pacific Design Automation Conference (ASPDAC 2023), 308-313. https://dx.doi.org/10.1145/3566097.3567846
Project: MOE2019-T2-1-071 
MOE2019-T1-001-072 
NAP (M4082282) 
Conference: 28th Asia and South Pacific Design Automation Conference (ASPDAC 2023)
Abstract: Current communication infrastructures for convolutional neural networks (CNNs) only focus on specific transmission patterns, not applicable to benefit the whole system if the dataflow changes or different dataflows run in one system. To reduce data movement, various CNN dataflows are presented. For these dataflows, parameters and results are delivered using different traffic patterns, i.e., multicast, unicast, and gather, preventing dataflow-specific communication backbones from benefiting the entire system if the dataflow changes or different dataflows run in the same system. Thus, in this paper, we propose MUG-NoC to support typical traffic patterns and accelerate them, therefore boosting multiple dataflows. Specifically, (i) we for the first time support multicast in 2D-mesh software configurable NoC by revising router configuration and proposing the efficient multicast routing; (ii) we decrease unicast latency by transmitting data through the different routes in parallel; (iii) we reduce output gather overheads by pipelining basic dataflow units. Experiments show that at least our proposed design can reduce 39.2% total data transmission time compared with the state-of-the-art CNN communication backbone.
URI: https://hdl.handle.net/10356/165562
DOI: 10.1145/3566097.3567846
Schools: School of Computer Science and Engineering 
Rights: © 2023 Association for Computing Machinery. All rights reserved. This paper was published in the Proceedings of the 28th Asia and South Pacific Design Automation Conference (ASPDAC 2023) and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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