- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources4
- Resource Type
-
00040
- Availability
-
40
- Author / Contributor
- Filter by Author / Creator
-
-
Wan, Qingzhou (4)
-
Xiong, Feng (4)
-
Erickson, John R. (3)
-
Benosman, Ryad (2)
-
Rasetto, Marco (2)
-
Sharbati, Mohammad T. (2)
-
Akolkar, Himanshu (1)
-
Li, Yiyang (1)
-
Reilly, Matthew T. (1)
-
Shah, Vivswan (1)
-
Shao, Qiming (1)
-
Shi, Bertram (1)
-
Velagala, Sridhar Reddy (1)
-
Wang, Kang L. (1)
-
Youngblood, Nathan (1)
-
Zhang, Peng (1)
-
#Tyler Phillips, Kenneth E. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Adams, S.G. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
- (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Erickson, John R. ; Shah, Vivswan ; Wan, Qingzhou ; Youngblood, Nathan ; Xiong, Feng ( , Optics Express)
-
Wan, Qingzhou ; Zhang, Peng ; Shao, Qiming ; Sharbati, Mohammad T. ; Erickson, John R. ; Wang, Kang L. ; Xiong, Feng ( , APL Materials)
-
Wan, Qingzhou ; Rasetto, Marco ; Sharbati, Mohammad T. ; Erickson, John R. ; Velagala, Sridhar Reddy ; Reilly, Matthew T. ; Li, Yiyang ; Benosman, Ryad ; Xiong, Feng ( , Advanced Intelligent Systems)
Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three‐terminal, Li
x WO3‐based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time‐dependent synaptic functions such as paired‐pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike‐encoded timing information extracted from the short‐term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO‐STP synapses.