DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation

• URL: http://arxiv.org/abs/2608.01343v1
• Date: Sun, 02 Aug 2026 16:08:44 GMT
• Title: DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation
• Abstract summary: This paper presents DeVIT, an acceleration method for vision transformers that leverages differential computation to enable multiplier-less matrix multiplication.<n>Deep learning models have brought unprecedented performance across various domains, particularly in natural language processing and computer vision.
• Score: 1.0195618602298682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of transformer-based deep learning models has brought unprecedented performance across various domains, particularly in natural language processing and computer vision. However, deploying these models, especially on resource-constrained devices, poses significant challenges due to their high computational complexity and large memory size and bandwidth requirements. This complexity has led researchers to use low-bit model weights to reduce memory usage and improve efficiency. In addition to reducing processing and memory demands, quantization introduces another useful property: value locality, where the extremely large number of parameters are restricted to a limited range of values. To fully take advantage of this locality, this paper presents DeVIT, an acceleration method for vision transformers that leverages differential computation to enable multiplier-less matrix multiplication.

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