VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration

• URL: http://arxiv.org/abs/2506.01166v1
• Date: Sun, 01 Jun 2025 20:59:20 GMT
• Title: VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
• Authors: Shereef Helal, Alberto Garcia-Ortiz, Lennart Bamberg,
• Abstract summary: VUSA is a systolic-array architecture that virtually grows based on the present sparsity to perform larger matrix multiplications.<n>The proposed architecture achieves saving by 37% and 68% in area and power efficiency, respectively, at the same peak-performance.
• Score: 0.49157446832511503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging high degrees of unstructured sparsity is a promising approach to enhance the efficiency of deep neural network DNN accelerators - particularly important for emerging Edge-AI applications. We introduce VUSA, a systolic-array architecture that virtually grows based on the present sparsity to perform larger matrix multiplications with the same number of physical multiply-accumulate MAC units. The proposed architecture achieves saving by 37% and 68% in area and power efficiency, respectively, at the same peak-performance, compared to a baseline systolic array architecture in a commercial 16-nm technology. Still, the proposed architecture supports acceleration for any DNN with any sparsity - even no sparsity at all. Thus, the proposed architecture is application-independent, making it viable for general-purpose AI acceleration.

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