Hopfield Networks in Non-volatile Memory

Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumvent this bottleneck by performing the processing within the memory. Neural Networks are memory-bound, and a notable example of such is the Hopfield Network. This work explores the design space of non-volatile computing-in-memory (NV-CiM) architectures using an instruction-level simulator to evaluate application mappings and workload-driven architectural adaptations. The exploration examines the resulting design trade-offs and includes theoretical comparisons with representative CPU and GPU architectures.