Reconfigurable Energy-Efficient Circuits for Scalable Analog Neural Networks

Abstract

The increasing demand for machine learning at the edge and the rise of resource-constrained environments have created the need for innovative hardware platforms that can achieve high efficiency while maintaining computational scalability. Analog neural networks (ANNs) have emerged as a compelling alternative to digital counterparts due to their natural ability to exploit device physics for matrix-vector operations, leading to significant reductions in energy consumption. However, fixed analog implementations often lack the flexibility required for dynamic workloads and scalability across different models. This paper explores the design and optimization of reconfigurable energy-efficient circuits for scalable analog neural networks. The proposed framework combines adaptive circuit topologies, analog memory elements, and low-power interconnects to enable tunability in both network depth and width without compromising energy efficiency. Experimental evaluations are carried out on prototype circuits designed in 65nm CMOS technology, demonstrating substantial improvements in power efficiency, throughput, and scalability compared to traditional analog and mixed-signal solutions. The results highlight the potential of reconfigurable circuits to serve as the foundation for future edge-AI deployments where adaptability and energy efficiency are paramount.