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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
arXiv - CS - Hardware Architecture Pub Date : 2020-07-11 , DOI: arxiv-2007.05657 Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, and Giacomo Indiveri
arXiv - CS - Hardware Architecture Pub Date : 2020-07-11 , DOI: arxiv-2007.05657 Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, and Giacomo Indiveri
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic
processors, new opportunities are emerging for applying deep and Spiking Neural
Network (SNN) algorithms to healthcare and biomedical applications at the edge.
This can facilitate the advancement of the medical Internet of Things (IoT)
systems and Point of Care (PoC) devices. In this paper, we provide a tutorial
describing how various technologies ranging from emerging memristive devices,
to established Field Programmable Gate Arrays (FPGAs), and mature Complementary
Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL
accelerators to solve a wide variety of diagnostic, pattern recognition, and
signal processing problems in healthcare. Furthermore, we explore how spiking
neuromorphic processors can complement their DL counterparts for processing
biomedical signals. After providing the required background, we unify the
sparsely distributed research on neural network and neuromorphic hardware
implementations as applied to the healthcare domain. In addition, we benchmark
various hardware platforms by performing a biomedical electromyography (EMG)
signal processing task and drawing comparisons among them in terms of inference
delay and energy. Finally, we provide our analysis of the field and share a
perspective on the advantages, disadvantages, challenges, and opportunities
that different accelerators and neuromorphic processors introduce to healthcare
and biomedical domains. This paper can serve a large audience, ranging from
nanoelectronics researchers, to biomedical and healthcare practitioners in
grasping the fundamental interplay between hardware, algorithms, and clinical
adoption of these tools, as we shed light on the future of deep networks and
spiking neuromorphic processing systems as proponents for driving biomedical
circuits and systems forward.
中文翻译:
面向医疗保健和生物医学应用的深度网络加速器的硬件实现
随着专用深度学习 (DL) 加速器和神经形态处理器的出现,出现了将深度和尖峰神经网络 (SNN) 算法应用于边缘医疗保健和生物医学应用的新机会。这可以促进医疗物联网 (IoT) 系统和护理点 (PoC) 设备的发展。在本文中,我们提供了一个教程,描述了从新兴的忆阻器件到成熟的现场可编程门阵列 (FPGA) 和成熟的互补金属氧化物半导体 (CMOS) 技术的各种技术如何用于开发高效的深度学习加速器,以解决广泛的问题。医疗保健中的各种诊断、模式识别和信号处理问题。此外,我们探索了尖峰神经形态处理器如何补充其 DL 处理器以处理生物医学信号。在提供所需的背景之后,我们统一了对应用于医疗保健领域的神经网络和神经形态硬件实现的稀疏分布研究。此外,我们通过执行生物医学肌电图 (EMG) 信号处理任务并在推理延迟和能量方面比较它们之间的比较,对各种硬件平台进行了基准测试。最后,我们提供了对该领域的分析,并分享了对不同加速器和神经形态处理器引入医疗保健和生物医学领域的优势、劣势、挑战和机遇的看法。本文可以为大量读者提供服务,包括纳米电子研究人员、
更新日期:2020-07-14
中文翻译:
面向医疗保健和生物医学应用的深度网络加速器的硬件实现
随着专用深度学习 (DL) 加速器和神经形态处理器的出现,出现了将深度和尖峰神经网络 (SNN) 算法应用于边缘医疗保健和生物医学应用的新机会。这可以促进医疗物联网 (IoT) 系统和护理点 (PoC) 设备的发展。在本文中,我们提供了一个教程,描述了从新兴的忆阻器件到成熟的现场可编程门阵列 (FPGA) 和成熟的互补金属氧化物半导体 (CMOS) 技术的各种技术如何用于开发高效的深度学习加速器,以解决广泛的问题。医疗保健中的各种诊断、模式识别和信号处理问题。此外,我们探索了尖峰神经形态处理器如何补充其 DL 处理器以处理生物医学信号。在提供所需的背景之后,我们统一了对应用于医疗保健领域的神经网络和神经形态硬件实现的稀疏分布研究。此外,我们通过执行生物医学肌电图 (EMG) 信号处理任务并在推理延迟和能量方面比较它们之间的比较,对各种硬件平台进行了基准测试。最后,我们提供了对该领域的分析,并分享了对不同加速器和神经形态处理器引入医疗保健和生物医学领域的优势、劣势、挑战和机遇的看法。本文可以为大量读者提供服务,包括纳米电子研究人员、