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arXiv · 2510.14393

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow

Ching-Lin Hsiung·Tian-Sheuan Chang
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Abstract

Current transformer accelerators primarily focus on optimizing self-attention due to its quadratic complexity. However, this focus is less relevant for vision transformers with short token lengths, where the Feed-Forward Network (FFN) tends to be the dominant computational bottleneck. This paper presents a low power Vision Transformer accelerator, optimized through algorithm-hardware co-design. The model complexity is reduced using hardware-friendly dynamic token pruning without introducing complex mechanisms. Sparsity is further improved by replacing GELU with ReLU activations and employing dynamic FFN2 pruning, achieving a 61.5\% reduction in operations and a 59.3\% reduction in FFN2 weights, with an accuracy loss of less than 2\%. The hardware adopts a row-wise dataflow with output-oriented data access to eliminate data transposition, and supports dynamic operations with minimal area overhead. Implemented in TSMC's 28nm CMOS technology, our design occupies 496.4K gates and includes a 232KB SRAM buffer, achieving a peak throughput of 1024 GOPS at 1GHz, with an energy efficiency of 2.31 TOPS/W and an area efficiency of 858.61 GOPS/mm2.

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Ching-Lin Hsiung, Tian-Sheuan Chang. 2025-10-16. Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow. https://doi.org/10.1109/tcsi.2025.3585054

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