CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

arXiv:2304.05949v3 Announce Type: replace-cross
Abstract: Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs …

abstract algorithms arxiv become class cmos computers cond-mat.mes-hall cs.ai cs.et cs.lg inference law machine machine learning metal optimization quantum quantum simulation sampling semiconductor simulation stochastic type