This April 2021 academic paper from NVIDIA discusses the challenge of designing converged GPUs that efficiently handle the diverging architectural demands of High Performance Computing (HPC), which uses higher precision arithmetic, and Deep Learning (DL), which increasingly uses low precision math. The authors propose a new architecture called a Composable On-PAckage GPU (COPA-GPU), which uses multi-chip module disaggregation to create domain-specialized products that maximize design reuse. COPA-GPUs enable DL specialization by adding features like significantly larger on-package caches and higher DRAM bandwidth, which the analysis shows are critical for scaling DL performance where converged designs face memory bottlenecks. This new approach aims to provide superior cost-performance efficiency for both application domains, particularly in large-scale DL training scenarios. Source: https://arxiv.org/pdf/2104.02188