PLASTER: 7 Key Ways to Measure Deep Learning Performance NVIDIA Blog

But when it comes to deep learning and artificial intelligence, how do we capture the tangible standards that need to be met for an AI-based service to be successful? This acronym, introduced by NVIDIA CEO Jensen Huang at the GPU Technology Conference earlier this year, describes a framework that addresses the seven major challenges for delivering AI-based servers: Programmability, Latency, Accuracy, Size of Model, Throughput, Energy Efficiency and Rate of Learning. NVIDIA introduced this framework to help customers overcome these important challenges, given the complexity of deploying deep learning solutions and the rapidly moving pace of the industry. "Hyperscale data centers are the most complicated computers the world has ever made," Huang said. They serve hundreds of millions of people making billions of queries, and represent billions of dollars of investment.