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基于分布对齐和纠正的二进制变压器.
Kaili Wang1,2, Mingtao Wang1,2, Zixin Wan1,2
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种用于轻型变压器的新型二进制量化方法,解决了分布转移问题. 这种方法提高了嵌入式AI传感器的效率,而不会影响准确度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 变压器是强大的AI模型,具有高的计算需求,限制了它们在资源有限的嵌入式系统中的使用.
- 二元量子化提供了减少的内存和更快的计算,但面临着诸如轻型变压器中的分布转移等挑战.
研究的目的:
- 为适合嵌入式智能传感器的轻型变压器开发一种有效的二进制量化技术.
- 为了解决当前变压器二元化方法固有的分布转移问题.
主要方法:
- 研究了用于二进制化的特征分布对齐操作.
- 设计中位移和平均恢复以对齐二进制和全精度变压器特征分布.
- 开发了一个知识蒸架构,使用教师-学生模型 (全精度和二进制变压器) 来进行分布校正.
主要成果:
- 拟议的二进制优化模型有效地解决了分布转移问题.
- 在CIFAR10,CIFAR100,ImageNet-1k和TinyImageNet上的实验结果显示出卓越的性能.
- 该模型在相应的计算复杂度下优于现有的最先进的二进制化方法.
结论:
- 新的二进制量化方法提高了嵌入式智能传感器中变压器的可行性.
- 特征分布对齐和知识蒸方法确保数据的完整性和准确性.
- 这项工作在边缘设备的高效深度学习方面取得了重大进展.
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