概括
我们开发了一种轻量级的神经网络模型,用于使用8位整数定量化的计算机生成全息 (CGH). 这大大减少了模型大小,并提高了增强现实显示器的速度.
科学领域:
- 计算机视觉 计算机视觉 计算机视觉
- 全息影像的使用方法.
- 机器学习是机器学习.
背景情况:
- 计算机生成全息 (CGH) 对增强现实 (AR) 显示至关重要,但面临着计算方面的挑战.
- 神经网络加速CGH,但嵌入式系统需要高效的模型.
研究的目的:
- 开发一种轻量级的神经网络模型,用于复杂的全息图生成.
- 为了降低嵌入式CGH的计算成本,内存足迹和功耗.
主要方法:
- 引入了神经网络量化,特别是将张量体全息模型从32位浮点 (FP32) 转换为8位整数 (INT8) 精度.
- 评估全息图质量,模型大小和处理速度.
- 在嵌入式部署的系统模块上实现了INT8模型.
主要成果:
- INT8模型实现了与FP32模型相提并论的全息质量.
- 模型大小减少了大约70%.
- 处理速度增加了四倍,证明了在嵌入式平台上的高功率效率和可部署性.
结论:
- 神经网络量子化为高效的CGH提供了有效的解决方案.
- 开发的INT8模型适用于资源有限的设备上的实时AR应用.
- 这种方法克服了传统CGH在实际AR显示器实现中的局限性.
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