Energy efficient neuromorphic computing circuit and architecture design
In recent years, fast computation, low power, and scalability are the key motivations for building SNN hardware. However, the unique features of SNN hardware have not been fully exploited, where the computation speed and energy efficiency of the SNN hardware can be improved. Firstly, this thesis pre...
Saved in:
Main Author: | Pu, Junran |
---|---|
Other Authors: | Goh Wang Ling |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/159982 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Energy efficient circuits and architectural design for machine learning on edge
by: Chong, Yi Sheng
Published: (2023) -
High energy efficient ultra-low voltage SRAM design : device, circuit, and architecture
by: Kim, Tony Tae-Hyoung, et al.
Published: (2013) -
DELTRON : neuromorphic architectures for delay based learning
by: Hussain, Shaista, et al.
Published: (2013) -
Low-power neuromorphic circuits for unsupervised spike based learning
by: He, Tong
Published: (2016) -
Low-power circuits for neuromorphic vision sensor based internet of video things
by: Zhang, Xueyong
Published: (2021)