Stochastic CBRAM-Based Neuromorphic Time Series Prediction System
Abstract
In this research, we present a Conductive-Bridge RAM (CBRAM)-based neuromorphic system which efficiently addresses time series prediction. We propose a new (i) voltage-mode, stochastic, multiweight synapse circuit based on experimental bi-stable CBRAM devices, (ii) a voltage-mode neuron circuit based on the concept of charge sharing, and (iii) an optimized training methodology powered by a stochastic implementation of the Least-Mean-Squares (SLMS) training rule. To validate the proposed design, we use time series prediction for short-term electrical load forecasting in smart grids. Our system is able to forecast hourly electrical loads with a mean accuracy of 96%, an estimated power dissipation of 15 μW, and area of 14.5 μm 2 at 65 nm CMOS technology.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 09, 2017
- Source ID
- 10.1145/2996193
Entities
People
- Bryant Wysocki
- Cory Merkel
- Dhireesha Kudithipudi
- Manan Suri
Organizations
- Air Force Office of Scientific Research
- Air Force Research Laboratory
- Indian Institutes of Technology
- Rochester Institute of Technology