Abstract: A probability distribution corresponding to the kernel function is determined and weights are sampled from the determined probability distribution corresponding to the given kernel function. Memristive devices of an analog crossbar are programmed based on the sampled weights, where each memristive device of the analog crossbar is configured to represent a corresponding weight. Two matrix-vector multiplication operations are performed on an analog input x and an analog input y using the programmed crossbar and a dot product is computed on results of the matrix-vector multiplication operations.

Abstract: A 3D compute-in-memory accelerator system and method for efficient inference of Mixture of Expert (MoE) neural network models. The system includes a plurality of compute-in-memory cores, each in-memory core including multiple tiers of in-memory compute cells. One or more tiers of in-memory compute cells correspond to an expert sub-model of the MoE model. One or more expert sub-models are selected for activation propagation based on a function-based routing, the tiers of the corresponding experts being activated based on this function. In one embodiment, this function is a hash-based tier selection function used for dynamic routing of inputs and output activations. In embodiments, the function is applied to select a single expert or multiple experts with input data-based or with layer-activation-based MoEs for single tier activation. Further, the system is configured as a multi-model system with single expert model selection or with a multi-model system with multi-expert selection.