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
