Memristive Device Variability Performance Impact on Neuromorphic Machine Learning Hardware
| Autor: | Rashmi Jha, Andrew J. Ford |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry Gaussian Memristor Machine learning computer.software_genre Perceptron law.invention Resistive random-access memory symbols.namesake Neuromorphic engineering law symbols Artificial intelligence Crossbar switch Resilience (network) business computer |
| Zdroj: | IGSC (Workshops) |
| DOI: | 10.1109/igsc51522.2020.9291114 |
| Popis: | A vital issue regarding hardware implementations of machine learning algorithms with novel memristive devices is the concern of the proposed architecture's resilience to high device variability. We find that most algorithms have surprisingly high tolerance to variable weight updates and initializations. We also propose a simple method to validate Single Layer Perceptron (SLP) neuromorphic hardware based on memristive RRAM crossbar arrays by studying accuracy vs. training time. Finally, we show high level simulations of an RRAM cell with intermediate states, decay, and Gaussian variability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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