复杂估值神经网络的FPGA实施,用于极地表示图像分类
Maruf Ahmad1, Lei Zhang1, Muhammad E H Chowdhury2
1Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了一种新的复杂值神经网络 (CVNN) 在现场可编程门数组 (FPGA) 上,用于高效的图像分类. 基于FPGA的CVNN实现了卓越的速度和功率效率,优于关键应用的现有模型.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 现有的神经网络模型在能源和资源效率方面存在局限性.
- 现场可编程门阵列 (FPGA) 提供了硬件加速的潜力.
- 复杂值神经网络 (CVNNs) 提供了先进的架构可能性.
研究的目的:
- 开发一个节能和资源优化的图像分类系统.
- 探索在FPGA上部署CVNN以提高性能.
- 解决当前神经网络模型在速度和功耗方面的局限性.
主要方法:
- 实现了一种新的卡特西安到极地图像转换,以减少数据量.
- 专门为FPGA实现设计和优化了一个CVNN模型.
- 在图像分类的MNIST数据集上评估了系统的性能.
主要成果:
- 开发的CVNN_128模型在MNIST测试数据集上实现了88.3%的准确性.
- 推断时间显著减少到1.6毫秒,功耗为4.66兆瓦.
- 与现有模型相比,该系统在分类速度和功率效率方面取得了超过100倍的改进.
结论:
- FPGA对CVNN的实施为图像分类提供了实质性的优势.
- 提出的方法在需要高速,低资源使用和最低功耗的场景中表现出色.
- 这项研究强调了有效的深度学习硬件加速的有希望的方向.
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