Computing with memory

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Computing with memory (or in-memory computing, IMC) is a paradigm that performs computations directly within memory arrays (RAM, RRAM, PCM) rather than transferring data to a separate CPU, significantly reducing energy consumption and latency. It addresses the von Neumann bottleneck by enabling data-parallel operations, such as for neural networks and big data analytics, at speeds up to thousands of times faster than traditional storage methods. These paradigms can follow either a purely spatial computing model or a temporal computing model. The latter approach aims at reducing overheads in FPGAs. Computing with memory differs from computing in memory or processor-in-memory concepts.

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