Design and Optimization of Energy-Efficient Circuits for On-Chip Neural Computation

Abstract

The rapid expansion of artificial intelligence (AI) applications has led to a demand for specialized hardware that can support neural computation while remaining energy-efficient. Traditional digital processors struggle with the high computational requirements of deep learning workloads, prompting a transition toward on-chip accelerators tailored for neural networks. Among the critical challenges is the design of circuits that minimize power consumption while maintaining performance and scalability. This paper explores the design principles and optimization techniques for energy-efficient circuits targeting on-chip neural computation. It analyzes trade-offs between digital and analog implementations, evaluates architectural strategies such as approximate computing and memory-centric design, and presents experimental results highlighting improvements in power efficiency and throughput. Through circuit-level optimizations and algorithm-hardware co-design, the study demonstrates that significant energy savings can be achieved without sacrificing computational accuracy. This work provides insights into the future direction of low-power AI hardware and its applicability to edge intelligence and real-time inference systems.