An Integrated Framework for High-Precision and Low-Power Computing

Abstract

Purpose: The goal of this research is to create a unified computational framework that addresses the shortcomings of IEEE-754 floating-point arithmetic by enhancing the equilibrium among precision, dynamic range, and hardware resource efficiency. Design / Methodology / Approach: The proposed method integrates Posit number representation, Newton-Raphson-based division, SIMD-based parallel execution, and pipeline optimisation techniques into a single hybrid computational framework. Posit arithmetic is used to increase accuracy with fewer bits, and Newton-Raphson iterations are used instead of hardware dividers to reduce latency. The data were analysed via comparative performance evaluation through simulation experiments. Performance ratios, percentage improvements, and graphs were used to analyse the results and assess improvements in accuracy, speed, parallel processing, and energy efficiency. Research Limitation: The present implementation and assessment are confined to simulated or regulated computational settings and particular nonlinear workloads. The framework has not been validated across all floating-point workloads or various real-time hardware platforms, which may impact its generalisability. Findings: The input/design variables, processing variables, and performance/output variables cut the bit-width by 20–30% compared to IEEE-754 and make numbers more accurate by up to 35%. Precision up to 10−610^{-6}10−6 is reached within three Newton–Raphson iterations, which cuts division latency by 40–50%. SIMD execution increases throughput by 60–70% and energy efficiency by about 20%. Optimising the pipeline cuts latency by 30% to 40%. In general, the design reduces hardware space by up to 20% and improves energy efficiency by 18%. Practical Implication: The suggested framework can be used effectively in high-performance computing systems, embedded processors, and AI accelerators to achieve higher numerical accuracy, use less hardware space and power, and increase data throughput without complicating the architecture. Social Implication: The proposed arithmetic framework supports sustainable computing practices by enabling more accurate, energy-efficient computing. Originality / Value: This study introduces a unique combination of Posit arithmetic, iterative division, SIMD parallelisation, and pipelining optimisation into a single framework. The overall benefits to precision, latency, hardware area, and energy efficiency make it a unique and scalable solution for next-generation computing.