NMExplorer: An Efficient Exploration Framework for DIMM-based Near-Memory Tensor Reduction
Cong Li, Zhe Charles Zhou, Xingchen Li, Guangyu Sun, Dimin Niu · 2023
Various DIMM-based near-memory processing (DIMM-NMP) architectures have been proposed to accelerate tensor reduction. With careful evaluation, we find that diverse scenarios exhibit distinct performance on DIMM-NMP architectures adopting different design configurations. However, given a tensor reduction scenario, there is a lack of a fast and accurate solution to identify a proper DIMM-NMP architecture. To tackle this problem, we propose an efficient exploration framework called NMExplorer. Given a scenario and hardware parameters, NMExplorer can generate and explore a wide range of potential design configurations. Experiments show that the recommended designs can outperform state-of-the-art DIMM-NMP accelerators by up to 1.95× in performance and 3.69× in energy.