Content Addressable Memory

Content-addressable memory (CAM) aims to create systems that retrieve information based on partial or noisy input, mirroring aspects of human memory. Current research focuses on improving CAM efficiency and capacity, particularly through novel architectures like Hopfield networks and memristor-based implementations, and optimizing algorithms for training and inference, including those leveraging sparsity and product quantization. These advancements are driving progress in energy-efficient machine learning, particularly for applications like robotic manipulation, and enabling new approaches to in-memory computing for faster and more efficient AI.