Analytical modeling demonstrates efficiency trade-offs of sparsity techniques in AI hardware accelerators, highlighting essential design strategies to realize practical gains.
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.
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Shi et al. (2025) studied this question.
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