Jinming Zhang;Xuyan Wang;Yaoyao Ye;Dongxu Lyu;Guojie Xiong;Ningyi Xu;Yong Lian;Guanghui He
{"title":"M2M: 在多芯片架构上加速多个 DNN 的细粒度映射框架","authors":"Jinming Zhang;Xuyan Wang;Yaoyao Ye;Dongxu Lyu;Guojie Xiong;Ningyi Xu;Yong Lian;Guanghui He","doi":"10.1109/TVLSI.2024.3438549","DOIUrl":null,"url":null,"abstract":"With the advancement of artificial intelligence, the collaboration of multiple deep neural networks (DNNs) has been crucial to existing embedded systems and cloud systems, especially for automatic driving applications as well as augmented and virtual reality (AR/VR) applications. To trade off between cost and performance, chiplet-based DNN accelerators have emerged as a promising solution for accelerating DNN workloads. However, most existing mapping methods for multiple DNNs target for the monolithic chip, which fail to solve the problems faced by the emerging multi-chiplet architecture, such as the problems of distributed memory access, complex heterogeneous interconnect network, and the scaling-up of computing resources. In this work, we propose M2M, a fine-grained mapping framework for accelerating multiple DNNs on a multi-chiplet architecture. It includes a temporal and spatial task scheduling for reconfigurable dataflow accelerators and a communication-aware task mapping in a heterogeneous interconnect network. To enhance communication efficiency and reduce the overall latency, we further propose a fine-tuned quality-of-service (QoS) policy for network-on-package (NoP) links. To the best of our knowledge, this is the first fine-grained mapping framework for multiple DNNs on a multi-chiplet architecture. We implemented the proposed fine-grained mapping framework using genetic algorithm and simulated annealing algorithm. Experimental results show that our work achieves 7.18%–61.09% latency reduction under vision, language, and mixed workloads when compared with the state-of-the-art related work.","PeriodicalId":13425,"journal":{"name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"M2M: A Fine-Grained Mapping Framework to Accelerate Multiple DNNs on a Multi-Chiplet Architecture\",\"authors\":\"Jinming Zhang;Xuyan Wang;Yaoyao Ye;Dongxu Lyu;Guojie Xiong;Ningyi Xu;Yong Lian;Guanghui He\",\"doi\":\"10.1109/TVLSI.2024.3438549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the advancement of artificial intelligence, the collaboration of multiple deep neural networks (DNNs) has been crucial to existing embedded systems and cloud systems, especially for automatic driving applications as well as augmented and virtual reality (AR/VR) applications. To trade off between cost and performance, chiplet-based DNN accelerators have emerged as a promising solution for accelerating DNN workloads. However, most existing mapping methods for multiple DNNs target for the monolithic chip, which fail to solve the problems faced by the emerging multi-chiplet architecture, such as the problems of distributed memory access, complex heterogeneous interconnect network, and the scaling-up of computing resources. In this work, we propose M2M, a fine-grained mapping framework for accelerating multiple DNNs on a multi-chiplet architecture. It includes a temporal and spatial task scheduling for reconfigurable dataflow accelerators and a communication-aware task mapping in a heterogeneous interconnect network. To enhance communication efficiency and reduce the overall latency, we further propose a fine-tuned quality-of-service (QoS) policy for network-on-package (NoP) links. To the best of our knowledge, this is the first fine-grained mapping framework for multiple DNNs on a multi-chiplet architecture. We implemented the proposed fine-grained mapping framework using genetic algorithm and simulated annealing algorithm. Experimental results show that our work achieves 7.18%–61.09% latency reduction under vision, language, and mixed workloads when compared with the state-of-the-art related work.\",\"PeriodicalId\":13425,\"journal\":{\"name\":\"IEEE Transactions on Very Large Scale Integration (VLSI) Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Very Large Scale Integration (VLSI) Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10634307/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10634307/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
M2M: A Fine-Grained Mapping Framework to Accelerate Multiple DNNs on a Multi-Chiplet Architecture
With the advancement of artificial intelligence, the collaboration of multiple deep neural networks (DNNs) has been crucial to existing embedded systems and cloud systems, especially for automatic driving applications as well as augmented and virtual reality (AR/VR) applications. To trade off between cost and performance, chiplet-based DNN accelerators have emerged as a promising solution for accelerating DNN workloads. However, most existing mapping methods for multiple DNNs target for the monolithic chip, which fail to solve the problems faced by the emerging multi-chiplet architecture, such as the problems of distributed memory access, complex heterogeneous interconnect network, and the scaling-up of computing resources. In this work, we propose M2M, a fine-grained mapping framework for accelerating multiple DNNs on a multi-chiplet architecture. It includes a temporal and spatial task scheduling for reconfigurable dataflow accelerators and a communication-aware task mapping in a heterogeneous interconnect network. To enhance communication efficiency and reduce the overall latency, we further propose a fine-tuned quality-of-service (QoS) policy for network-on-package (NoP) links. To the best of our knowledge, this is the first fine-grained mapping framework for multiple DNNs on a multi-chiplet architecture. We implemented the proposed fine-grained mapping framework using genetic algorithm and simulated annealing algorithm. Experimental results show that our work achieves 7.18%–61.09% latency reduction under vision, language, and mixed workloads when compared with the state-of-the-art related work.
期刊介绍:
The IEEE Transactions on VLSI Systems is published as a monthly journal under the co-sponsorship of the IEEE Circuits and Systems Society, the IEEE Computer Society, and the IEEE Solid-State Circuits Society.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
To address this critical area through a common forum, the IEEE Transactions on VLSI Systems have been founded. The editorial board, consisting of international experts, invites original papers which emphasize and merit the novel systems integration aspects of microelectronic systems including interactions among systems design and partitioning, logic and memory design, digital and analog circuit design, layout synthesis, CAD tools, chips and wafer fabrication, testing and packaging, and systems level qualification. Thus, the coverage of these Transactions will focus on VLSI/ULSI microelectronic systems integration.