Hardware Accelerator Design for Healthcare Applications: Review and Perspectives

Hardware accelerators have gained immense popularity in recent times for a varied range of healthcare applications. With the growth of edge computing, a large number of sensors can be integrated to enable lightweight computing for processing information. Over the years, there has been significant improvement in deep learning algorithms that offer exciting opportunities for their deployment even in safety-critical biomedical and healthcare applications. A detailed review and discussion on multiple challenges in the design of hardware acceleration catering to healthcare applications are presented in this paper. A wide range of generalized novel architectures and devices offers certain distinct advantages over the conventional processing units. Despite this development, the power and resource constraints of these platforms create significant hindrances in the acceleration of these high-risk medical applications. An elaborate analysis of the range of solutions to overcome these obstacles as well as a perspective on the need to address reliability and security concerns are presented. An alternative correct-by-construction accelerator design methodology is also proposed.