System-Level Benchmarking of Chiplet-based IMC Architectures for Deep Neural Network Acceleration

In-memory computing (IMC) on a large monolithic chip for deep learning faces area, yield, and fabrication cost challenges due to the ever-increasing model sizes. 2.5D or chiplet-based architectures integrate multiple small chiplets to form a large computing system, presenting a feasible solution to accelerate large deep learning models. In this work, we present a novel benchmarking tool, SIAM, to ...