Why it matters: Existing deep learning resources lag behind the curve due to increased complexity, different resource needs, and limitations imposed by current hardware architectures. Several Nvidia researchers have recently published technology papers looking at changing corporate multi-chip modules (MCM) to meet these changing requirements. This article presents the team's position on the benefits of a Composable-On-Package (COPA) GPU for better adaptability to different types of deep learning workloads.
GPUs have become one of the primary sources of DL support due to their inherent capabilities and improvements. COPA-GPU is based on the understanding that traditional converged GPU designs are rapidly becoming less practical than practical solutions that use industry-specific hardware. These converged GPU solutions are based on an architecture made up of traditional formats as well as a set of specialized hardware such as high bandwidth memory (HBM), tensor cores (Nvidia) / matrix cores (AMD), packet tracking (RT) cores, and more. This convergent design results in devices that may be suitable for some tasks but ineffective for others. p>
Unlike current integrated GPU designs, which combine all specific executable and storage components into a single package, COPA - GPU architecture provides the ability to combine and adapt multiple hardware blocks to better suit the dynamic workloads presented In High Performance Computing (HPC) and Deep Learning (DL) environments. This ability to integrate more capabilities and adapt multiple workloads may lead to higher levels of GPU reuse and, most importantly, increase the ability of data scientists to push the boundaries of what is possible with their available resources. p>
in many Sometimes, though, the concepts of Artificial Intelligence (AI), Machine Learning (ML), and DL are completely different. DL, an AI and ML subsidiary, seeks to simulate the way the human brain manages information by using filters to predict and classify information. DL is the driving force behind many automated AI capabilities that can do everything from drive our cars to monitor financial systems for fraudulent activity. p>
They used the title of the next step in their work. The evolution of the CPU and GPU over the past few years - the concept of MCM is not very new. MCMs date back to IBM bubble memory MCMs and 3081 mainframe computers of the 1970s and 1980s. p>