Neuromorphic Architectures for Fast Low-Power Robot Perception, Work Unit IT015-09-41-1G25

Abstract

This memorandum summarizes the efforts of work unit 1G25 where new brain-inspired neuromorphic computing technology and deep/convolutional neural networks (CNN) where applied to develop real-time low SWaP scene understanding capabilities for mobile robotic systems. Specifically, we sought understanding of the relationships between the perception task, CNN-based algorithms, and the constraints of neuromorphic systems and to derive principles of CNN design for neuromorphic architectures.

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Document Details

Document Type
Technical Report
Publication Date
Jun 18, 2020
Accession Number
AD1101920

Entities

People

  • Joseph T. Hays
  • Keith M. Sullivan
  • Wallace E Lawson

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Command And Control
  • Computations
  • Control Systems
  • Convolutional Neural Networks
  • Department Of Defense
  • Distance Learning
  • Information Systems
  • Machine Learning
  • Military Research
  • Navigation
  • Neural Networks
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Autonomous System Control