Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware
Abstract
Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 30, 2021
- Source ID
- 10.1145/3451210
Entities
People
- Catherine D. Schuman
- Garrett S. Rose
- Md Musabbir Adnan
- Mst Shamim Ara Shawkat
- Sagarvarma Sayyaparaju
- Samuel D. Brown
Organizations
- Air Force Research Laboratory
- Oak Ridge National Laboratory
- United States Department of Energy
- University of Tennessee