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

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.

Authors

Institutions

Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/micro61859.2024.00038
Citations
2
Primary Topic
Interactive and Immersive Displays
Type
article
Field-Weighted Citation Impact
0.74
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yakun Sophia Shao, Hasan Genc, Prashanth Ganesh, Hansung Kim
2 citations
Interactive and Immersive Displays
0.74
article

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

Yakun Sophia Shao, Hasan Genc, Prashanth Ganesh, Hansung Kim
article en
2 citations

Abstract

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.

University of California, Berkeley (US)
Openalex Percentile: Top 26%
Interactive and Immersive Displays
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Stellar: An Automated Design Framework for Dense and Sparse Spatial Accelerators — Yakun Sophia Shao, Hasan Genc, et al. · (2024) | TGRS Research Map | TGRS