学位论文详细信息
A system design approach to neuromorphic classifiers | |
Analog signal processing;Machine learning;Bio-inspired | |
Ramakrishnan, Shubha ; Hasler, Jennifer Electrical and Computer Engineering Anderson, David Butera, Robert Culurciello, Eugenio Lim, Sung-Kyu ; Hasler, Jennifer | |
University:Georgia Institute of Technology | |
Department:Electrical and Computer Engineering | |
关键词: Analog signal processing; Machine learning; Bio-inspired; | |
Others : https://smartech.gatech.edu/bitstream/1853/51718/1/ramakrishnan_shubha_201305_phd.pdf | |
美国|英语 | |
来源: SMARTech Repository | |
【 摘 要 】
This work considers alternative strategies to mainstream digital approaches to signal processing - namely analog and neuromorphic solutions, for increased computing efficiency. In the context of a speech recognizer application, we use low-power analog approaches for the signal conditioning and basic auditory feature extraction, while using a neuromorphic IC for building a dendritic classifier that can be used as a low-power word spotter. In doing so, this work also aspires to posit the significance of dendrites in neural computation.
【 预 览 】
Files | Size | Format | View |
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A system design approach to neuromorphic classifiers | 5045KB | download |