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Memristor-Based Analog Neuromorphic Computing Engine Design and Robust Training Scheme
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liu, Beiye |
| Copyright Year | 2014 |
| Abstract | The invention of neuromorphic computing architecture is inspired by the working mechanism of human-brain. Memristor technology revitalized neuromorphic computing system design by efficiently executing the analog Matrix-Vector multiplication on the memristor-based crossbar (MBC) structure. In this work, we propose a memristor crossbar-based embedded platform for neuromorphic computing system. A variety of neural network algorithms with threshold activation function can be easily implemented on our platform. However, programming the MBC to the target state can be very challenging due to the difficulty to real-time monitor the memristor state during the training. In this thesis, we quantitatively analyzed the sensitivity of the MBC programming to the process variations and input signal noise. We then proposed a noise-eliminating training method on top of a new crossbar structure to minimize the noise accumulation during the MBC training and improve the trained system performance, i.e., the pattern recall rate. A digital-assisted initialization step for MBC training is also introduced to reduce the training failure rate as well as the training time. We also proposed a memristor-based bidirectional transmission exhibition/inhibition synapse and implemented neuromorphic computing demonstration with our proposed synapse. Experiment results show that the proposed design has high tolerance on process variation and input noise. Different benefits of MBC system and new synapse-based system will be compared in our thesis. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://d-scholarship.pitt.edu/20918/1/Liu_etd2014.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
