Novel in-memory computing accelerators for Machine Learning (ML) on the edge

We explore a novel general-purpose CPU design that tightly integrates analog in-memory computing accelerators in processor pipelines execution pipelines. The accelerator is able to execute accelerating matrix-vector multiplications, which dominate deep neural network applications, in constant time, while presenting a high degree of flexibility, allowing to seamlessly accommodate diverse workloads.

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