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By: Bibhu Prasad Ganthia and Rosalin Pradhan.
1,2. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India.
The continuous demand for high-speed, energy-efficient computing has intensified the exploration of in- memory analog computing (IMAC) architectures, which aim to minimize the latency and power overhead associated with conventional von Neumann architectures. This research investigates the integration of phase- change materials (PCMs) with semiconductor circuits to realize programmable, non-volatile in-memory analog computing platforms. PCMs exhibit reversible resistance switching between amorphous and crystalline states, enabling precise analog weight storage directly within computational elements. By embedding PCMs in CMOS- compatible circuits, the proposed architecture facilitates parallelized matrix-vector multiplication, critical for accelerating machine learning and signal processing tasks. Comprehensive device-level simulations demonstrate that PCM-integrated circuits can achieve low write energy (~pJ per operation) and high endurance (>10^9 cycles) while maintaining computational accuracy within 2% of ideal analog behavior. Additionally, circuit- level analyses reveal enhanced scalability and reduced area footprint compared to conventional SRAM-based analog computing units. In order to increase long-term dependability and computational efficiency, the study also looks at the thermal stability, switching dynamics, and data retention properties of PCM devices under various operating situations. The study provides a framework for optimizing PCM device parameters, circuit layouts, and analog computation strategies to maximize speed, energy efficiency, and reliability. These findings underscore the potential of PCM-enabled in-memory analog computing as a viable pathway for next-generation edge AI and neuromorphic systems.
Keywords – Phase-Change Material, In-Memory Analog Computing, PCM-Integrated Semiconductor Circuits, Non-Volatile Analog Computing, Energy-Efficient Neuromorphic Systems, Matrix-Vector Multiplication, Edge AI.
Citation:
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