Full CMOS-Memristor Implementation of a Dynamic Neuromorphic Architecture
Abstract
Neuromorphic computing systems seek to emulate biological neural functionality emulated in either software or electrical hardware. A key function for such systems is their ability to learn and adapt. In the human brain, such learning and adaptation is achieved via modulation of synaptic connections between different neurons. Memristors (implemented as resistive random access memory or ReRAM) have great potential to provide synaptic functionality for neuromorphic chip architectures. Under an AFRL-sponsored program, we have developed a unique memristor-CMOS hybrid system for implementing a dynamic adaptive neural network array, also known as mrDANNA. Most recently, our effort has moved from a software-based emulator, to FPGA implementation, and finally to the design, tapeout, and fabrication of this unique, adaptive approach to neuromorphic computing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 12, 2018
- Accession Number
- AD1052240
Entities
People
- Gangotree Chakma
- Garrett S. Rose
- J. Murray
- James S. Plank
- Joseph Van Nostrand
- Karsten Beckmann
- Mark Dean
- Mussabir Adnan
- Nathaniel C Cady
- Ryan Weiss
- Sagarvarma Sayyaparaju
- Sherif Amer
- Wilkie Olin-Ammentorp