Energy-Efficient On-Chip Analysis for Radiation Detection Applications Using Neuromorphic Algorithms and Systems
Abstract
Basic research is proposed on energy-efficient neuromorphic algorithms, architectures, and hardware capable of analyzing data generated by spectroscopic sensors with minimal power consumption. During the Base Period, we will develop a new application-specific neuromorphic algorithm inspired by a locally competitive spiking sparse approximation, build small-scale functional prototypes incorporating filamentary resistive random-access memory (ReRAM) arrays as a proof-of-concept, and test them in a real-world setup. During the Option Period, we will develop, build, and test more advanced devices and algorithms that directly harness the device properties, such as reservoir computing, as well as improve and optimize the prototypes. Demonstration hardware will incorporate nonfilamentary ReRAM arrays.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 16, 2019
- Source ID
- HDTRA11810009
Entities
People
- Marek Osinski
Organizations
- Defense Threat Reduction Agency
- University of New Mexico