PLASTER: A Framework for Deep Learning Performance

White Papers > Deep Learning > PLASTER: A Framework for Deep Learning Performance
deep learning

Machine learning (ML) is a key category in artificial intelligence (AI). Both hardware and software advances in deep learning (DL), a type of ML, appear to be catalysts for the early stages of a phenomenal AI growth trend. The challenge at this phase of adoption is twofold: deploying deep learning solutions is a complex proposition, and it is a rapidly moving target.

At the NVIDIA GPU Technology Conference (GTC) 2018, Jensen Huang, NVIDIA President and CEO, put forward the PLASTER framework to contextualize the key challenges delivering AI-based services.

“PLASTER” encompasses seven major challenges for delivering AI-based services.

  • Programmability
  • Latency
  • Accuracy
  • Size of Model
  • Throughput
  • Energy Efficiency
  • Rate of Learning

This paper explores each of these AI challenges in the context of NVIDIA’s DL solutions. PLASTER as a whole is greater than the sum of its parts. Anyone interested in developing and deploying AI-based services should factor in all of PLASTER’s elements to arrive at a complete view of deep learning performance.

Download the new white paper from NVIDIA that addresses the challenges described in PLASTER, which is important in any  deep learning solution, and it is especially useful for developing and delivering the inference engines underpinning AI-based services.

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