AHA: An Agile Approach to the Design of Coarse-Grained Reconfigurable Accelerators and Compilers

Author:

Koul Kalhan1ORCID,Melchert Jackson1ORCID,Sreedhar Kavya1ORCID,Truong Leonard1ORCID,Nyengele Gedeon1ORCID,Zhang Keyi1ORCID,Liu Qiaoyi1ORCID,Setter Jeff1ORCID,Chen Po-Han1ORCID,Mei Yuchen1ORCID,Strange Maxwell1ORCID,Daly Ross1ORCID,Donovick Caleb1ORCID,Carsello Alex1ORCID,Kong Taeyoung1ORCID,Feng Kathleen1ORCID,Huff Dillon1ORCID,Nayak Ankita1ORCID,Setaluri Rajsekhar1ORCID,Thomas James1ORCID,Bhagdikar Nikhil1ORCID,Durst David1ORCID,Myers Zachary1ORCID,Tsiskaridze Nestan1ORCID,Richardson Stephen1ORCID,Bahr Rick1ORCID,Fatahalian Kayvon1ORCID,Hanrahan Pat1ORCID,Barrett Clark1ORCID,Horowitz Mark1ORCID,Torng Christopher1ORCID,Kjolstad Fredrik1ORCID,Raina Priyanka1ORCID

Affiliation:

1. Stanford University, Stanford, California, USA

Abstract

With the slowing of Moore’s law, computer architects have turned to domain-specific hardware specialization to continue improving the performance and efficiency of computing systems. However, specialization typically entails significant modifications to the software stack to properly leverage the updated hardware. The lack of a structured approach for updating the compiler and the accelerator in tandem has impeded many attempts to systematize this procedure. We propose a new approach to enable flexible and evolvable domain-specific hardware specialization based on coarse-grained reconfigurable arrays (CGRAs). Our agile methodology employs a combination of new programming languages and formal methods to automatically generate the accelerator hardware and its compiler from a single source of truth. This enables the creation of design-space exploration frameworks that automatically generate accelerator architectures that approach the efficiencies of hand-designed accelerators, with a significantly lower design effort for both hardware and compiler generation. Our current system accelerates dense linear algebra applications but is modular and can be extended to support other domains. Our methodology has the potential to significantly improve the productivity of hardware-software engineering teams and enable quicker customization and deployment of complex accelerator-rich computing systems.

Funder

DSSoC DARPA

Stanford AHA Agile Hardware Center

Affiliates Program, Intel’s Science and Technology Center

Stanford SystemX Alliance

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FDRA: A Framework for a Dynamically Reconfigurable Accelerator Supporting Multi-Level Parallelism;ACM Transactions on Reconfigurable Technology and Systems;2024-01-27

2. Towards a Unified Implementation of GEMM in BLIS;Proceedings of the 37th International Conference on Supercomputing;2023-06-21

3. PRAD: A Bayesian Optimization-based DSE Framework for Parameterized Reconfigurable Architecture Design;2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2023-05

4. APEX: A Framework for Automated Processing Element Design Space Exploration using Frequent Subgraph Analysis;Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3;2023-03-25

5. Amber: A 16-nm System-on-Chip With a Coarse-Grained Reconfigurable Array for Flexible Acceleration of Dense Linear Algebra;IEEE Journal of Solid-State Circuits;2023

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