WeChat Mini Program
Old Version Features

Chip Implementation of Supervised Neural Network Using Single-Transistor Synapses

Microelectronics Journal(2017)

Chun Yuan Christian Univ

Cited 4|Views3
Abstract
In this work, the newly developed neural chip applied in analog inputs for on-chip training and recognition is presented. We have designed the neural chip using single-transistor synapses which are capable of storing analog weights. The neural chip includes the interface circuit, power switches, analog synaptic array (7 × 4 synapses), and transresistance amplifiers (TR_AMPs) for on-chip training and recognition. Voice signals were acquired using analog signal processing and conditioning circuits for use in verifying the chip's pattern recognition functionality. The experimental results reveal that the synaptic weights of the neural network have adapted with training and have gradually converged to the targets afterwards. Upon system convergence, the recognition rates of the targeted speaker and the three others were evaluated. By using very small amount of synapses, as few as 28 synapses, the system's successful recognition rate for the targeted speaker is 93.5% for 200 tests; whereas, the rate for the other speakers is approximately 6.3% for 600 tests.
More
Translated text
Key words
Single transistor synapse,Neural network chip,Pattern recognition
PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined