تقرير
Trainable Associative Memory Neural Networks in a Quantum-Dot Cellular Automata
| العنوان: | Trainable Associative Memory Neural Networks in a Quantum-Dot Cellular Automata |
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| المؤلفون: | Stovold, James |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Emerging Technologies |
| الوصف: | Quantum-dot cellular automata (QCAs) offer a diffusive computing paradigm with picosecond transmission speed, making them an ideal candidate for moving diffusive computing to real-world applications. By implementing a trainable associative memory neural network into this substrate, we demonstrate that high-speed, high-density associative memory is feasible through QCAs. The presented design occupies $415\text{nm}^2$ per neuron, which translates to circa $240 \text{ billion neurons/cm}^2$, or $28\text{GB/cm}^2$ of memory storage, offering a real possibility for large-scale associative memory circuits. Results are presented from simulation, demonstrating correct working behaviour of the associative memory in single neurons, two-neuron and four-neuron arrays. Comment: Pre-review version. Submitted to UCNC 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1901.00881 |
| رقم الأكسشن: | edsarx.1901.00881 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |