A CMOS-based Resistive Crossbar Array with Pulsed Neural Network for Deep Learning Accelerator
| Autor: | Byung-Geun Lee, Sang-Gyun Gi, Jung-Gyun Kim, Injune Yeo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Resistive touchscreen Artificial neural network Computer science business.industry Deep learning 02 engineering and technology Crossbar array Nonlinear system 020901 industrial engineering & automation CMOS 0202 electrical engineering electronic engineering information engineering Electronic engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | AICAS |
| DOI: | 10.1109/aicas.2019.8771576 |
| Popis: | A CMOS-based resistive computing element (RCE), which can be integrated in a crossbar array, is presented. The RCE successfully solves the hardware constraints of the existing memristive devices such as dynamic ranges of conductance, I-V nonlinearity, and on/off ratio without increasing hardware complexity compared to other CMOS implementations. The RCE has been designed using a 65nm standard CMOS process and SPICE simulations have been performed to evaluate feasibility and functionality of the RCE. In addition, a pulsed neural network employing an RCE crossbar array has also been designed and simulated to verify the operation of the RCE. |
| Databáze: | OpenAIRE |
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