Abstract:
Heterogeneous 3D compute-in-memory (CiM) architectures have emerged as an efficient acceleration paradigm for deep neural network (DNN) inference by leveraging the complementary characteristics of diverse memory technologies enabled through 3D integration. Prior work has focused on architectural efficiency and workload-mapping optimization, leaving the security implications of heterogeneous CiM systems largely …