Building a Comprehensive Neuromorphic Platform for Remote Computation
Abstract
Remote sensing and extreme environments present a unique and critical algorithm and hardware tradeoff due to extreme size, weight and power constraints. Consequently, in many applications, systems favor centralized computation over remote computation. However, in some real-time systems (e.g. a Mars rover) latency and other communication bottlenecks force on-board processing. With traditional processor performance at a plateau, we look to brain-inspired, non-Von Neumann neuromorphic architectures to enable future capabilities, such as event detection/tracking and intelligent decision making. These cutting-edge hardware platforms generally operate at vastly improved performance-per-Watt ratios, but have suffered from niche applications, difficult interfaces, and poor integration with existing algorithms. In this paper, we discuss methods, motivated by recent results, to produce a cohesive neuromorphic system that effectively integrates novel and traditional algorithms for context-driven remote computation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 25, 2019
- Accession Number
- AD1075285
Entities
People
- Aaron J. Hill
- Angel Yanguas-Gil
- Craig M. Vineyard
- Felix Wang
- James B. Aimone
- Leah Reeder
- Ryan Dellana
- William Severa
Organizations
- Sandia National Laboratories