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Application of Silicon-Germanium Source Tunnel-FET to Enable Ultralow Power Cellular Neural Network-Based Associative Memory
| Content Provider | Semantic Scholar |
|---|---|
| Author | Trivedi, Amit Ranjan Datta, Suman Mukhopadhyay, Saibal |
| Copyright Year | 2014 |
| Abstract | This paper studies the application of tunnel FET (TFET) in designing a low power and robust cellular neural network (CNN)-based associative memory (AM). The lower leakage, steeper switching slope, and higher output resistance of TFET are exploited in designing an ultralow-power TFET-based operational transconductance amplifier (OTA). A TFET-OTA is utilized as a programmable synaptic weight multiplier for CNN. The ultralow-power of TFET-OTA enables a higher connectivity network even at a lower power, and thereby improves the memory capacity and input pattern noise tolerance of CNN-AM for low power applications. The TFET-based higher connectivity CNN also exploits the unique characteristics of TFET to improve the throughput efficiency of CNN-AM. |
| Starting Page | 3707 |
| Ending Page | 3715 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/TED.2014.2357777 |
| Volume Number | 61 |
| Alternate Webpage(s) | http://www.ndcl.ee.psu.edu/papers/Trivedi_TFET%20CNN_Nov%202014.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/TED.2014.2357777 |
| Journal | IEEE Transactions on Electron Devices |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
