Memristive Crossbar

Memristive crossbar arrays are being explored as energy-efficient hardware accelerators for deep neural networks (DNNs), particularly for matrix-vector multiplications, a core operation in many machine learning models. Current research focuses on mitigating the inherent noise and variability of memristive devices through techniques like optimized bit encoding, novel pruning algorithms, and architectural modifications to DNNs (e.g., adapting transformers and spiking neural networks for crossbar implementation). This research aims to overcome limitations in accuracy and reliability, paving the way for faster, more power-efficient edge computing and potentially revolutionizing applications ranging from image recognition to neuromorphic computing.