MUGNoC A Software-Configured Multicast-Unicast-Gather NoC for Accelerating CNN Dataflows
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...
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| Published in: | 2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC) pp. 308 - 313 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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New York, NY, USA
ACM
16.01.2023
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| Series: | ACM Conferences |
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| ISBN: | 9781450397834, 1450397832 |
| ISSN: | 2153-697X |
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| 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. |
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| AbstractList | 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, param-eters and results are delivered using different traffic patterns, i.e., multicast, unicast, and gather, preventing dataflow-specific commu-nication 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. 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. |
| Author | Luo, Xiangzhong Li, Shiqing Liu, Di Chen, Hui Liu, Weichen Huai, Shuo |
| Author_xml | – sequence: 1 givenname: Hui surname: Chen fullname: Chen, Hui email: hui.chen@ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 2 givenname: Di surname: Liu fullname: Liu, Di email: liu.di@ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 3 givenname: Shiqing surname: Li fullname: Li, Shiqing email: shiqing.li@ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 4 givenname: Shuo surname: Huai fullname: Huai, Shuo email: shuo.huai@ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 5 givenname: Xiangzhong surname: Luo fullname: Luo, Xiangzhong email: xiangzho001@ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 6 givenname: Weichen surname: Liu fullname: Liu, Weichen email: liu@ntu.edu.sg organization: Nanyang Technological University, Singapore |
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| Keywords | parallel multipath transmission CNN dataflow network-on-chips |
| Language | English |
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| PublicationTitle | 2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC) |
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| Snippet | Current communication infrastructures for convolutional neural networks (CNNs) only focus on specific transmission patterns, not applicable to benefit the... |
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| StartPage | 308 |
| SubjectTerms | Boosting CNN Dataflow Design automation Network-on-chips Networks Networks -- Network algorithms Networks -- Network algorithms -- Control path algorithms Networks -- Network algorithms -- Control path algorithms -- Network control algorithms Networks -- Network architectures Networks -- Network architectures -- Network design principles Networks -- Network components Networks -- Network protocols Networks -- Network protocols -- Network layer protocols Networks -- Network services Networks -- Network services -- Programmable networks Networks -- Network types Networks -- Network types -- Packet-switching networks Parallel multipath transmission Routing Software System performance Traffic control Unicast |
| Subtitle | A Software-Configured Multicast-Unicast-Gather NoC for Accelerating CNN Dataflows |
| Title | MUGNoC |
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