Resistive RAM (RRAM) device has been extensively used as a scalable nonvolatile memory cell in neuromorphic systems due to its several advantages, including its small size and low-power requirements. However, resulting from the stochastic nature of the oxygen vacancies, the RRAM device suffers from reliability soft errors. In this paper, we provide for the first time a modeling framework to compute the effect of those soft errors on the system accuracy. Applying the proposed technique on a case-study system used to recognize the MNIST data set, our simulation results show that the system accuracy can degrade from 91.6% to 43% due to the RRAM reliability soft errors. To overcome this loss in the system performance, various possible adjustments to the parameters of the neuron pulses are analyzed. Furthermore, in this paper, two methodologies are proposed for automatically detecting and fixing the degradation in the system accuracy caused by the RRAM reliability soft errors. Using the suggested methodologies, the system accuracy of our case-study system can be restored back from 43% to 91.6% with small increase in the training cycle duration and with as small as 0.1% increment in the energy consumption of the system.
|Original language||English (US)|
|Journal||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|State||Accepted/In press - Aug 15 2017|
- Hafnium compounds
- MNIST recognition system
- neuromorphic systems
- nonvolatile memory
- Pattern recognition
- Performance evaluation
- resistive RAM (RRAM) arrays
- RRAM reliability soft errors.
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering