Sparsity-Aware Hardware: From Overheads to Performance Benefits

• Published in: IEEE solid state circuits magazine Vol. 17; no. 2; pp. 61 - 71
• Main Authors: Shi, Man, Kneip, Adrian, Chauvaux, Nicolas, Sun, Jiacong, Frenkel, Charlotte, Verhelst, Marian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI accelerators; Analytical models; Artificial intelligence; Computational efficiency; Computational modeling; Hardware; Integrated circuit modeling; Transforms
• ISSN: 1943-0582; 1943-0590
• DOI: 10.1109/MSSC.2025.3549709

As artificial intelligence (AI) continues to transform multiple sectors, its exponential growth in computational demands presents significant challenges for hardware infrastructure. This article examines sparsity, the prevalence of zeros in AI workloads, as a promising approach to address these challenges. While sparsity offers potential efficiency gains, its practical implementation requires careful consideration of hardware constraints and computational overheads. Therefore, this article cooperates with a virtual performance roofline model to analyze various sparsity techniques and their associated tradeoffs, aiming to bridge the gap between theoretical potential and practical implementation in AI accelerator design.