Convolutional Neural Network on Embedded Linux System-on-Chip: A Methodology and Performance Benchmark

Abstract

Deep convolutional neural networks (CNNs) detect and classify features of interest in sensory input data. There is a need to investigate how best to implement CNNs for Navy and Department of Defense (DoD) use in platforms with minimal size, weight, and power (SWaP) capacity, since much academic research focuses solely on achieving the highest performance on a specific dataset with minimal concern of compute resources. This report describes a methodology, configuration, and experimental results of a first step in this studya baseline for comparison of benchmarking metrics. A baseline is important for quantifying any further results and to estimate potential benefits of new and more advanced ideas.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2016
Accession Number
AD1009093

Entities

People

  • Daniel Gebhardt
  • Iryna Dzieciuch
  • Keyur Parikh

Organizations

  • Naval Information Warfare Systems Command

Tags

Communities of Interest

  • Advanced Electronics
  • Autonomy
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computers
  • Computing System Architectures
  • Convolutional Neural Networks
  • Department Of Defense
  • Digital Signal Processing
  • Embedded Systems
  • Field Programmable Gate Arrays
  • Governments
  • Information Operations
  • Instruction Set Architecture
  • Instructions
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Operating Systems
  • Signal Processing
  • United States Government

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Radio communications and signal processing.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks