Approximate Computing Architectures for Low-Power VLSI

Abstract

The growing demand for energy-efficient electronic systems in domains such as mobile computing, Internet of Things (IoT), and artificial intelligence has led to the exploration of unconventional design methodologies. Approximate computing has emerged as a promising paradigm, trading off exactness for significant improvements in power efficiency, performance, and silicon area. This paper investigates the role of approximate computing architectures in low-power VLSI design. It highlights the theoretical foundation, techniques, and applications while providing an analysis of recent architectural innovations. Through comparative evaluations, approximate computing is shown to enable substantial energy savings, particularly in error-tolerant applications such as multimedia processing, data mining, and machine learning. The study concludes that approximate computing, when carefully applied, offers a practical pathway toward next-generation energy-efficient VLSI architectures.

KEYWORDS: Approximate Computing, Low-Power VLSI, Energy-Efficient Design, Error-Tolerant Applications, Hardware Optimization