研究生: |
詹凱鈞 Chan, Kai-Chun |
---|---|
論文名稱: |
利用電阻保護氧化層場效電晶體雜訊電流產生器實現隨機性Izhikevich神經元電路 Implementation of Stochastic Izhikevich Neuron Circuit Based on Resist-Protection-Oxide Field-Effect Transistor Noise Current Generator |
指導教授: |
陳新
Chen, Hsin |
口試委員: |
金雅琴
King, Ya-Chin 彭盛裕 Peng, Sheng-Yu |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電子工程研究所 Institute of Electronics Engineering |
論文出版年: | 2022 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 71 |
中文關鍵詞: | 電阻保護氧化層場效電晶體 、Izhikevich 神經元 |
外文關鍵詞: | RPOFET, Izhikevich Neuron |
相關次數: | 點閱:1 下載:0 |
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由於人工智慧演算法需要極大的運算量,以致現行的硬體實現都具
有功耗較高的缺點。幸運地是, 受到生物神經為低功耗系統的啟發, 突
波式神經網路即是時下有話題性的研究方向之一,若能妥善的運用其長
處於演算法的硬體實現,想必是一強大的優勢。
我們發現神經元能夠透過雜訊進行機率型運算,而提高學習效率。
而在電路的實現上,可以透過電晶體本身的雜訊,模仿神經元的機率型
運算行為。
本論文旨在改良及設計以電阻保護氧化層場效電晶體(RPOFET)雜
訊電流產生器的方式是否可實現隨機性 Izhikevich 神經元模型。經由兩
次的晶片下線與量測驗證, RPOFET雜訊電流產生器皆未能使Izhikevich
神經元產生符合泊松分布的突波序列,主因為雜訊電流產生器電路設計
不良及高估 RPOFET 本身的雜訊能力。
Because the artificial intelligence algorithm requires a huge amount of
computation, the current hardware implementation has the disadvantage of
high power consumption. Fortunately, inspired by the fact that biological
nerves are low-power systems, the spurious neural network is one of the most
topical research directions. is a powerful advantage.
We found that neurons can perform probabilistic operations through
noise, which improves learning efficiency.
In the realization of the circuit, the probabilistic operation behavior of neurons
can be imitated through the noise of the transistor itself.
This paper aims to improve and design whether the random Izhikevich
neuron model can be realized by means of resistive protection oxide field
effect transistor (RPOFET) noise current generator. After two times of chip
off-line and measurement verification, the RPOFET noise current generator
failed to make the Izhikevich neuron generate a surge sequence conforming
to the Poisson distribution, mainly due to the poor circuit design of the noise
current generator and the overestimation of the RPOFET itself noise
capability
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