ACES: Accelerating Sparse Matrix Multiplication with Adaptive Execution Flow and Concurrency-Aware Cache Optimizations

Lu, Xiaoyang; Long, Boyu; Chen, Xiaoming; Han, Yinhe; Sun, Xian-He

doi:10.1145/3620666.3651381

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

DOI: 10.1145/3620666.3651381

|View full text |Cite

ACES: Accelerating Sparse Matrix Multiplication with Adaptive Execution Flow and Concurrency-Aware Cache Optimizations

Xiaoyang Lu,

Boyu Long,

Xiaoming Chen

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient SpMM Accelerator for Deep Learning: Sparkle and Its Automated Generator

Xu,

Jiang,

et al. 2024

ACM Trans. Reconfigurable Technol. Syst.

View full text Add to dashboard Cite

Deep learning (DL) technology has made breakthroughs in a wide range of intelligent tasks such as vision, language, recommendation systems, etc. Sparse matrix multiplication (SpMM) is the key computation kernel of most sparse models. Conventional computing platforms such as CPUs, GPUs, and AI chips with regular processing units are unable to effectively support sparse computation due to their fixed structure and instruction sets. This work extends Sparkle, an accelerator architecture, which is developed specifically for processing SpMM in DL. During the balanced data loading process, some modifications are implemented to enhance the flexibility of the Sparkle architecture. Additionally, a Sparkle generator is proposed to accommodate diverse resource constraints and facilitate adaptable deployment. Leveraging Sparkle’s structural parameters and template-based design methods, the generator enables automatic Sparkle circuit generation under varying parameters. An instantiated Sparkle accelerator is implemented on the Xilinx xqvu11p FPGA platform with a specific configuration. Compared to the state-of-the-art SpMM accelerator SIGMA, the Sparkle accelerator instance improves the sparse computing efficiency by about 10 to 20 \(\%\) . Furthermore, the Sparkle instance achieved 7.76 \(\times\) higher performance over the Nvidia Orin NX GPU. More instances of accelerators with different parameters were evaluated, demonstrating that the Sparkle architecture can effectively accelerate SpMM.

show abstract

Efficient SpMM Accelerator for Deep Learning: Sparkle and Its Automated Generator

Xu,

Jiang,

et al. 2024

ACM Trans. Reconfigurable Technol. Syst.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

ACES: Accelerating Sparse Matrix Multiplication with Adaptive Execution Flow and Concurrency-Aware Cache Optimizations

Cited by 1 publication

References 44 publications

Efficient SpMM Accelerator for Deep Learning: Sparkle and Its Automated Generator

Efficient SpMM Accelerator for Deep Learning: Sparkle and Its Automated Generator

Contact Info

Product

Resources

About