Stellar: An Automated Design Framework for Dense and Sparse Spatial Accelerators
Hasan Nazim Genc; Hansung Kim; Prashanth Ganesh; Yakun Sophia Shao
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.