DIAL ACCELERATORS FOR DEEP NEURAL NETWORKS: DISTRIBUTED AND NETWORKED, IN-EMBEDDED-HARDWARE, ADVERSARIAL GENERATIVE, LOW-PRECISION ARITHMETIC LEARNING AND INFERENCE

Abstract

The Recipient will seek to perform research that exploits a recent opportunity that special-purpose deep neural networks (DNNs) accelerators for devices, edges, and the cloud can serve as general-purpose computing engines, given that deep learning is widely applicable to many problems. This research will develop solutions to two pressing problems: easing the development and adoption of novel accelerators for Artificial Intelligence (AI) computations and creating trustworthy AI systems guided by physical insights.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 22, 2022
Source ID
FA87502210500

Entities

People

  • H.t. Kung

Organizations

  • President and Fellows of Harvard College
  • Rome Laboratory
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks