3DNN-Xplorer: A Machine Learning Framework for Design Space Exploration of Heterogeneous 3-D DNN Accelerators

This article presents 3DNN-Xplorer, the first machine learning (ML)-based framework for predicting the performance of heterogeneous 3-D deep neural network (DNN) accelerators. Our ML framework facilitates the design space exploration (DSE) of heterogeneous 3-D accelerators with a two-tier compute-on-memory (CoM) configuration, considering 3-D physical design factors. Our design space encompasses four distinct heterogeneous 3-D integration styles, combining 28- and 16-nm technology nodes for both compute and memory tiers. Using extrapolation techniques with ML models trained on 10-to-256 processing element (PE) accelerator configurations, we estimate the performance of systems featuring 75–16384 PEs, achieving a maximum absolute error of 13.9% (the number of PEs is not continuous and varies based on the accelerator architecture). To ensure balanced tier areas in the design, our framework assumes the same number of PEs or on-chip memory capacity across the four integration styles, accounting for area imbalance resulting from different technology nodes. Our analysis reveals that the heterogeneous 3-D style with 28-nm compute and 16-nm memory is energy-efficient and offers notable energy savings of up to 50% and an 8.8% reduction in runtime compared to other 3-D integration styles with the same number of PEs. Similarly, the heterogeneous 3-D style with 16-nm compute and 28-nm memory is area-efficient and shows up to 8.3% runtime reduction compared to other 3-D styles with the same on-chip memory capacity.