Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.18547 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909721338839040 |
|---|---|
| author | Lin, Ching-Yi Shah, Sahil |
| author_facet | Lin, Ching-Yi Shah, Sahil |
| contents | Pre-trained vision transformers have achieved remarkable performance across various visual tasks but suffer from expensive computational and memory costs. While model quantization reduces memory usage by lowering precision, these models still incur significant computational overhead due to the dequantization before matrix operations. In this work, we analyze the computation graph and propose an integerization process based on operation reordering. Specifically, the process delays dequantization until after matrix operations. This enables integerized matrix multiplication and linear module by directly processing the quantized input. To validate our approach, we synthesize the self-attention module of ViT on a systolic array-based hardware. Experimental results show that our low-bit inference reduces per-PE power consumption for linear layer and matrix multiplication, bridging the gap between quantized models and efficient inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Bit Integerization of Vision Transformers using Operand Reordering for Efficient Hardware Lin, Ching-Yi Shah, Sahil Machine Learning Computer Vision and Pattern Recognition Systems and Control Pre-trained vision transformers have achieved remarkable performance across various visual tasks but suffer from expensive computational and memory costs. While model quantization reduces memory usage by lowering precision, these models still incur significant computational overhead due to the dequantization before matrix operations. In this work, we analyze the computation graph and propose an integerization process based on operation reordering. Specifically, the process delays dequantization until after matrix operations. This enables integerized matrix multiplication and linear module by directly processing the quantized input. To validate our approach, we synthesize the self-attention module of ViT on a systolic array-based hardware. Experimental results show that our low-bit inference reduces per-PE power consumption for linear layer and matrix multiplication, bridging the gap between quantized models and efficient inference. |
| title | Low-Bit Integerization of Vision Transformers using Operand Reordering for Efficient Hardware |
| topic | Machine Learning Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2504.18547 |