In-memory computing is an emerging technology in AI. In traditional computer architectures, data is stored on a disc and called into memory when needed. Memory then gives the CPU access to the data. With in-memory computing, certain computational tasks are performed in memory. This reduces the latency and energy waste of moving data around and significantly improves performance.

A concept called at-memory computing developed by startup Untether uses short, massively parallel direct connections between specially optimized memory and the processor. Graphcore has developed an intelligence processing unit (IPU) that holds the entire machine learning model within the processor. Celestial AI is developing an AI accelerator chip with light-based logic systems.