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Updated: Jul 23, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
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尼龙:最快的方法,有效地在边缘执行您的深度学习算法
IEEE journal of biomedical and health informatics
|July 17, 2023
概括
在深度学习 (DL) 模型中,NERONE可实现无的现场可编程门阵列 (FPGA) 加速,提高能源效率而不会改变开发工作流程. 与临床应用中的图形处理单元 (GPU) 相比,这种解决方案可节省大量的电力.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 硬件加速器 硬件加速器
背景情况:
- 深度学习 (DL) 模型对于临床应用至关重要,例如辐射剂量定量和手术规划.
- 图形处理单元 (GPU) 常用于DL推断,但耗电密集,限制其在受限制环境中的使用.
- 现场可编程门阵列 (FPGA) 提供优越的每瓦性能,但对于非专家来说,使用它们是具有挑战性的.
研究的目的:
- 推出NERONE,这是一种简化DL模型FPGA加速的工具.
- 为了使最终用户能够在不改变现有的DL开发流程的情况下利用FPGA的能源效率.
- 为了证明NERONE在不同网络架构和FPGA板上的多功能性.
主要方法:
- 为各种数据集开发了四种DL模型 (三个用于细分,一个用于分类).
- 在使用NERONE框架的嵌入式FPGA板上部署这些模型.
- 与移动和数据中心GPU相比,评估了能源效率的改进.
主要成果:
- NERONE促进了FPGAs用于DL推断的使用,使它们能够被更广泛的受众访问.
- 与移动GPU相比,平均能效提高了3.4倍.
- 与数据中心GPU相比,平均能效提高1.9倍.
结论:
- NERONE有效地弥合了DL开发和FPGA硬件加速之间的差距.
- 该工具提高了临床DL应用的能源效率,特别是在功率受限的环境中.
- NERONE支持各种网络架构,展示了其适应性和广泛适用性.
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