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AIoT and Deep Neural Network-Based Accelerators for Healthcare and Biomedical Applications
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Author(s): Jothimani K. (Graphic Era University, India)and Bhagya Jyothi K. L. (KVG College of Engineering, India)
Copyright: 2023
Pages: 19
Source title:
Energy Systems Design for Low-Power Computing
Source Author(s)/Editor(s): Rathishchandra Ramachandra Gatti (Sahyadri College of Engineering and Management, India), Chandra Singh (Sahyadri College of Engineering and Management, India), Srividya P. (RV College of Engineering, India)and Sandeep Bhat (Sahyadri College of Engineering and Management, India)
DOI: 10.4018/978-1-6684-4974-5.ch007
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Abstract
Convolutional neural network (CNN) systems have an increasing number of applications in healthcare and biomedical edge applications due to the advent of deep learning accelerators and neuromorphic workstations. AIoT and sense of care (SOC) medical technology development may benefit from this. In this chapter, the authors show how to develop deep learning accelerators to address healthcare analytics, pattern classification, and signal processing problems using emerging restrictive gadgets, field programmable gate arrays (FPGAs), and metal oxide semiconductors (CMOS). Neuromorphic processors are compared with DL counterparts when it comes to processing biological signals. In this study, the authors focus on a range of hardware systems that incorporate data from electromyography (EMG) and computer vision. Inferences are compared using neuromorphic processors as well as integrated AI accelerators. In the discussion, the authors examined the issues and benefits, downsides, difficulties, and possibilities that various acceleration and neuromorphic processors bring to medicine and biomedicine.
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