Hardware Implementation of a High-Precision Fixed-Point MAC for Biomedical Application

Abstract

Biomedical signal processing for implantable and wearable technology imposes a crucial design conundrum: operating within strict power and area constraints while obtaining the numerical preci-sion required for clinical validity. With an emphasis on applications like ECG, EMG, and EEG processing, this paper offers a thorough analysis of high-precision Fixed-Point Multiply-Accumulate (MAC) architectures as the key solution to this problem. First, we determine the precision-quantization trade-off by examining the signal-to-noise ratio (SNR) and dynamic range requirements of important biomedical algorithms, such as wavelet transforms, adaptive filters, and light-weight neural networks. Although floating-point units offer inherent dynamic range, optimised fixed-point alternatives are required due to their excessive resource costs. Three axes are used in our review to systematically classify and evaluate MAC architectures: accumulation strategies (guard bits, extended-precision adders), computational core (modified Booth encoding, Wallace/Dadda trees), and system-level optimisation (bit-serial processing, precision-scalable units). We assess their implementation effectiveness and suitability for both ASIC (for ultimate power/area optimisation) and FPGA (for rapid prototyp-ing) targets. With the help of case studies that quantify the effects of MAC architecture decisions on diagnostic metrics like detection sen-sitivity and feature extraction accuracy in real-time biomedical sys-tems, the paper ends with a synthesis of co-design approaches that connect algorithm development and hardware implementation. The goal of this review is to give researchers and engineers a unified frame-work for designing effective, high-fidelity signal processing cores for the upcoming generation of autonomous medical devices.