在二进制神经网络中优化数据流
Lorenzo Vorabbi1,2, Davide Maltoni2, Stefano Santi1
1Datalogic Labs, Via San Vitalino 12, 40012 Bologna, BO, Italy.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
二进制神经网络 (BNNs) 通过使用比特智能运算来加速人工智能. BNN-Clip提高了数据流量,降低了计算成本,实现了更快的推断速度,具有可比的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 二进制神经网络 (BNNs) 通过比特式运算提供了显著的推断加速,取代了浮点算法.
- 目前的BNN方法通常会因为中间位宽转换 (1到16/32位) 而阻碍数据流效率.
研究的目的:
- 引入BNN-Clip,这是一个新的培训计划,旨在增强BNN管道中的并行性和数据流.
- 为了减少BNNs中的计算开销和延迟,而不会影响准确性.
主要方法:
- 实现了一个剪切块,在BNN层内将数据宽度从32位减少到8位.
- 从32位降低了二进制层的内部累积器大小,以减轻溢出风险而不会损失准确度.
- 优化批量规范化层,以减少延迟和简化部署.
- 为ARM NEON指令集开发了一个优化的二进制直接卷积实现.
主要成果:
- 与最先进的BNN框架相比,实现了一致的推断延迟加速度,高达1.3x和2.4x.
- 保持了与基准数据集 (CIFAR-10,SVHN,ImageNet) 的现有最先进方法相匹配的准确性.
结论:
- BNN-Clip有效地提高了二进制神经网络的效率和速度.
- 提出的方法为在资源有限的设备上部署高性能BNN提供了一种实际方法.
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