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MICRO2024顶会

Stellar: An Automated Design Framework for Dense and Sparse Spatial Accelerators

Hasan Nazim Genc, Hansung Kim, Prashanth Ganesh, Yakun Sophia Shao

2024年份
3被引次数
3顶会引用

摘要

Domain-specific hardware, coupled with co-designed algorithmic optimizations, plays a pivotal role in accelerating both dense and sparse workloads, surpassing the capability of general-purpose platforms. However, the diverse nature of these specialized hardware platforms makes it challenging to systematically implement, evaluate, and compare different solutions.

To address these shortcomings, we introduce Stellar, a novel accelerator design framework tailored for dense and sparse spatial accelerators. Stellar introduces abstractions that systematically decouple different dimensions of accelerator design, addressing the need for a clear separation of concerns for automated design solutions. This modular approach enhances the clarity and flexibility of the design process, while enabling automated hardware generation for a range of dense and sparse accelerator designs. Stellar outputs synthesizable Verilog implementations of these accelerators, paired with RISC-V programming interfaces. We demonstrate that Stellar-generated accelerators are comparable to hand-written, high-quality hardware designs, enabling effective evaluation, comparison, and design-space exploration for both dense and sparse accelerators.

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