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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications ACCEPTED BY IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2020 1 Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications Mostafa Rahimi Azghadi , Senior Member, IEEE, Corey Lammie , Student Member, IEEE, Jason K. Eshraghian , Member, IEEE, Melika Payvand , Member, IEEE, Elisa Donati , Member, IEEE, Bernabe´ Linares-Barranco , Fellow, IEEE and Giacomo Indiveri , Senior Member, IEEE Abstract—The advent of dedicated Deep Learning (DL) ac- the reliability of Deep Learning (DL) improves, it has per- celerators and neuromorphic processors has brought on new vaded various facets of healthcare from monitoring [3], [4], to opportunities for applying both Deep and Spiking Neural Net- prediction [5], diagnosis [6], treatment [7], and prognosis [8]. work (SNN) algorithms to healthcare and biomedical appli- cations at the edge. This can facilitate the advancement of Fig. 1(a) shows how data collected from the patient, which medical Internet of Things (IoT) systems and Point of Care may be a combination of bio-samples, medical images, tem- (PoC) devices. In this paper, we provide a tutorial describing perature, movement, etc., can be processed using a smart DL how various technologies including emerging memristive devices, system that monitors the patient for anomalies and/or to predict Field Programmable Gate Arrays (FPGAs), and Complementary diseases. DL systems can be used to recommend treatment Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, options and prognosis, which further affect monitoring and pattern recognition, and signal processing problems in healthcare. prediction in a closed-loop scenario. Furthermore, we explore how spiking neuromorphic processors The capacity of Artificial Intelligence (AI) to meet or can complement their DL counterparts for processing biomedical exceed the performance of human experts in medical-data signals. The tutorial is augmented with case studies of the vast analysis [9], [10], [11] can, in part, be attributed to the literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hard- continued improvement of high-performance computing plat- ware platforms by performing a sensor fusion signal processing forms such as Graphics Processing Units (GPUs) [12] and task combining electromyography (EMG) signals with computer customized Machine Learning (ML) hardware [13]. These can vision. Comparisons are made between dedicated neuromorphic now process and learn from a large amount of multi-modal processors and embedded AI accelerators in terms of inference la- heterogeneous general and medical data [14]. This was not tency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, readily achievable a decade ago. and opportunities that various accelerators and neuromorphic While the field of DL has been growing at an astonishing processors introduce to healthcare and biomedical domains. rate in terms of performance, network size, and training run Index Terms—Spiking Neural Networks, Deep Neural Net- time, the development of dedicated hardware to process DL works, Neuromorphic Hardware, CMOS, Memristor, FPGA, algorithms is struggling to keep up. Concretely, the compute RRAM, Healthcare, Medical IoT, Point-of-Care loads of DL have doubled every 3.4 months since 2012. Moore’s Law targets the doubling of compute power every I. INTRODUCTION 18-24 months, and appears to be slowing down [15]. The arXiv:2007.05657v2 [cs.AR] 28 Apr 2021 progress in hardware accelerator development currently relies RTIFICIAL intelligence is uniquely poised to cope with on advances by a handful of technology companies, most the growing demands of the universal healthcare sys- A notably Nvidia and its GPUs [16], [17] and Google and its tem [1]. The healthcare industry is projected to reach over Tensor Processing Units (TPUs) [13], in addition to new 10 trillion dollars by 2022, and the associated workload on startups and research groups developing Application Specific medical practitioners is expected to grow concurrently [2]. As Integrated Circuits (ASICs) for DL training and acceleration. M. Rahimi Azghadi and Corey Lammie are with the College of Science While there are significant advances in tailoring deep network and Engineering, James Cook University, QLD 4811, Australia. e-mail: models and algorithms for various healthcare and biomedical [email protected] J. K. Eshraghian is with the Department of Electrical Engineering and applications [18], most computationally expensive deep net- Computer Science, The University of Michigan, Ann Arbor, MI 48109-2122, works are trained on either GPUs or in data centers [12], USA. [19]. The latter typically requires access to cloud computing M. Payvand, E. Donati, and G. Indiveri are with the Institute of Neuroin- formatics, University and ETH Zurich, Switzerland. services which is not only costly and comes with high power B. Linares-Barranco is with the Instituto de Microelectronica´ de demands, but also compromises data privacy. This is distinct Sevilla IMSE-CNM (CSIC and Universidad de Sevilla), Sevilla, Spain. to the effective deployment of DL at the edge on an increasing © This work is licensed under a Creative Commons Attribution 4.0 License. number of medical IoT devices [20] and PoC systems [21], as For more information, see https://creativecommons.org/licenses/by/4.0/ illustrated in Fig. 1(b). Edge learning and inference enables the ACCEPTED BY IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2020 2 Fig. 1. A depiction of (a) the usage of DL in a smart healthcare setting, which typically involves monitoring, prediction, diagnosis, treatment, and prognosis. The various parts of the DL-based healthcare system can run on (b) the three levels of the IoT, i.e. edge devices, edge nodes, and the cloud. However, for healthcare IoT and PoC processing, edge learning and inference is preferred. option to move processing away from the cloud. This is critical resource-intensive. This will justify the need for application for highly sensitive medical data and offline operation. Edge- specific hardware platforms. After that, we discuss recent based processing must combine compactness, low-power, and hardware advances which have led to improvements in training rapid (high throughput) at a low-cost, to make smart health and inference efficiency. These improvements ultimately guide monitoring viable and affordable for integration into human us to viable edge inference engine options. life [22]. After reviewing the literature on these DL accelerators, we Specialized embedded DL accelerators, such as the Nvidia quantify the performance of various algorithms on different Jetson and Xavier series [23], and the Movidius Neural Com- types of DL processors. The results allow us to draw a pute Stick [24], [25], have shown the promise of edge comput- perspective on the potential future of spike-based neuromor- ing. More recently, the Nvidia Clara Embedded was released phic processors in the biomedical signal processing domain. as a healthcare-specific edge accelerator. This is a computing Based on our analysis and perspective, we conjecture that, platform for edge-enabled AI on the Internet of Medical for edge processing, neuromorphic computing and Spiking Things (IoMT). However, embedded devices remain relatively Neural Networks (SNNs) [26] will likely complement DL power hungry and costly, and many state-of-the-art algorithms inference engines, either through signaling anomalies in the far exceed the memory bandwidth of resource-constrained data or acting as ‘intelligent always-on watchdogs’ which devices. They are not yet ideal learning/inference engines continuously monitor the data being recorded, but only activate for ambient-assisted precision medicine systems. There is a further processing stages if and when necessary. need for innovative systems which can satisfy the stringent We expect this tutorial, review and perspective to provide requirements of healthcare edge devices to be made affordable guidance on the history and future of DL accelerators, and the to the community at large scales. potential they hold for advancing healthcare. Our contributions To that end, in this paper we focus on the use of three vari- are summarized as follows: ous hardware technologies to develop dedicated deep network • Our paper is the first to discuss the use of three differ- accelerators which will be discussed from a biomedical and ent emerging and established hardware technologies for healthcare application point-of-view. The three technologies facilitating DL acceleration, with a focus on biomedical that we cover here are CMOS, memristors, and Field Pro- applications. grammable Gate Arrays (FPGAs). It is worth noting that, while • We provide tutorial sections on how one may implement our focus targets edge inference engines in the biomedical a typical biomedical task on FPGAs or simulate it for domain, the techniques and hardware advantages discussed deployment on memristive crossbars. here are likely to be useful for efficient offline deep network • Our paper is the first to discuss how event-based neuro- learning, or online on-chip learning. Herein, the term DL morphic processors can complement DL accelerators for ‘accelerator’ is used to refer to a device that is able to perform biomedical signal processing. DL inference and potentially training. • We provide
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