Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications

This research focuses on critical hardware components of an Internet of Things (IoT) system for reconfigurable processing systems. Single-Instruction Multiple-Data (SIMD) processors have recently been utilized to preprocess data at energy-constrained sensor nodes or IoT gateways, saving significant energy and bandwidth for transmission. Using traditional CPU-based systems to implement machine learning algorithms is inefficient in terms of energy consumption. In the proposed method Single-Instruction Multiple-Data (SIMD) processors are assembled by scaling the largest possible operand value subunits into direct access to the internal memory, where the carry output of each unit is conditionally fed into the next unit based on the implementation of the SIMD Processor design for Internet of Things applications. Each method has evaluated sub-operations that contribute considerably to the overall potential of the design. If the single register file can complete the intended action, a zero (one)-signal is applied to each unit's carry input. Multiplexers combine two or more adders, sending the carry signal from one unit into another if additional units are necessary to compute the sum. The outcome results compare high-speed end device techniques in terms of area and power consumption. The proposed SIMD processor-based IoT healthcare monitoring system with a MIMD processor's performance analysis of comparison clearly demonstrates that the system produces decent outcomes. The suggested system has an area overhead of 85 [m.sup.2], a power usage of 4.10 W, and a time delay of 20 ns.