Stellar: An Automated Design Framework for Dense and Sparse Spatial Accelerators
Hasan Nazim Genc, Hansung Kim, Prashanth Ganesh, Yakun Sophia Shao
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ee3f3b5d-7c29-4f92-9041-a16ba0b8ae9aCited by top-tier papers3
- Empowering Vector Architectures for ML: The CAMP Architecture for Matrix MultiplicationMohammadreza Esmali Nojehdeh, Hossein Mokhtarnia, Julian Pavon, Narcís Rodas et al.MICRO 2025 · 1 citation
- RTeAAL Sim: Using Tensor Algebra to Represent and Accelerate RTL SimulationYan Zhu, Boru Chen, Christopher W. Fletcher, Nandeeka NayakASPLOS 2026 · 1 citation
- SegFold: Accelerating Sparse Gemm with a Fine-Grained Dynamic DataflowXinrui Wu, Hanyu Wang, Jason Cong, Tony NowatzkiISCA 2026
Builds on13
- SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN TrainingEric Qin, Ananda Samajdar, Hyoukjun Kwon, Vineet Nadella et al.HPCA 2020 · 490 citations
- SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head PruningHanrui Wang, Zhekai Zhang, Song HanHPCA 2021 · 412 citations
- Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack IntegrationHasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali et al.DAC 2021 · 325 citations
- Learning N: M Fine-grained Structured Sparse Neural Networks From ScratchAojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu et al.ICLR 2021 · 301 citations
- AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload RebalancingTong Geng, Ang Li, Runbin Shi, Chunshu Wu et al.MICRO 2020 · 299 citations
Related papers
- DSAGEN: Synthesizing Programmable Spatial AcceleratorsJian Weng, Sihao Liu, Vidushi Dadu, Zhengrong Wang et al.ISCA 2020 · 140 citations
- TensorLib: A Spatial Accelerator Generation Framework for Tensor AlgebraLiancheng Jia, Zizhang Luo, Liqiang Lu, Yun LiangDAC 2021 · 49 citations
- TeAAL: A Declarative Framework for Modeling Sparse Tensor AcceleratorsNandeeka Nayak, Toluwanimi O. Odemuyiwa, Shubham Ugare, Christopher W. Fletcher et al.MICRO 2023 · 19 citations
- HASCO: Towards Agile HArdware and Software CO-design for Tensor ComputationQingcheng Xiao, Size Zheng, Bingzhe Wu, Pengcheng Xu et al.ISCA 2021 · 73 citations
- AMOS: enabling automatic mapping for tensor computations on spatial accelerators with hardware abstractionSize Zheng, Renze Chen, Anjiang Wei, Yicheng Jin et al.ISCA 2022 · 63 citations
