Abstract
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) 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. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.
Highlights
A RTIFICIAL intelligence is uniquely poised to cope with the growing demands of the universal healthcare system [1]
4) Memristive Device Nonidealities: ideal memristive crossbars have been projected to remarkably accelerate Deep Neural Networks (DNNs) learning and inference and drastically reduce their power consumption [101], [102], device imperfections observed in experimentally fabricated memristors impose significant performance degradation when the crossbar sizes are scaled up for deployment in real-world DNN architectures, such as those required for healthcare and biomedical applications discussed in Subsection III-A
A non-exhaustive list of these obstacles include: (i) the power and resource constraints of available mobile platforms which, despite significant improvements, are still not suitable for highrisk medical tasks; (ii) the need to verify that a Deep Learning (DL) system can generalize beyond the distribution they are trained and tested on; (iii) bias that is inherent to datasets which may have adverse impacts on classification across different populations; (iv) confusion surrounding the liability of Artificial Intelligence (AI) algorithms in high-risk environments [132]; and (v) the lack of a streamlined workflow between medical practitioners and DL
Summary
A RTIFICIAL intelligence is uniquely poised to cope with the growing demands of the universal healthcare system [1]. Based on our analysis and perspective, we conjecture that, for edge processing, neuromorphic computing and SNNs [26] will likely complement DL inference engines, either through signaling anomalies in the data or acting as ‘intelligent always-on watchdogs’ which continuously monitor the data being recorded, but only activate further processing stages if and when necessary We expect this tutorial, review and perspective to provide guidance on the history and future of DL accelerators, and the potential they hold for advancing healthcare. We demonstrate the steps and techniques required to simulate and implement hardware for the benchmark hand-gesture classification task using memristive crossbars and FPGAs. In Section IV, we provide our perspective on the challenges and opportunities of both DNNs and SNNs for biomedical applications and shed light on the future of spiking neuromorphic hardware technologies in the biomedical domain.
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