應用於電子鼻系統之連續侷限型波茲曼演算法晶片設計

Design of Continuous Restricted Boltzmann Machine IC for Electronic Nose System

王仁和(JEN-HUO WANG)

指導教授 ： 陳新

國立清華大學/電機資訊學院/電機工程學系/碩士(2013年)

摘要

許多可攜式或是植入式的微系統裝置依據其不同的生醫領域應用，會和感測器陣列結合，而這些感測器的反應通常是高維度且充滿雜訊或是飄移的，為了要讓植入式裝置能直接於生物體內進行診斷或是減少無線傳輸的資料量，會需要一個低功耗且能即時做訊號前處理的內嵌式裝置。在各種訊號處理演算法中，機率模型能夠使用其運算的隨機性來概括資料本身的變異，有助於系統進行穩健的訊號辨識，而連續侷限型波茲曼演算法，使用隨機型類神經網路的架構，已經於先前文獻中展現了優異且穩健的生醫訊號處理能力，並且能夠透過其學習能力即時調整參數維持良好的分群降維效果，非常適合扮演系統中訊號前處理的腳色。
本論文主要探討將連續侷限型波茲曼演算法應用於電子鼻系統作為訊號前處理裝置的方法，首先以軟體進行模擬驗證連續侷限型波茲曼演算法處理電子鼻感測訊號的可行性，接著進一步研究將演算法實現成硬體的方法，而演算法硬體已經成功以TSMC 0.18μm與90nm製程實現，並整合入電子鼻系統當中，使用LabVIEW軟體以及資料擷取卡系統設計實驗平台，以氣體資料進行量測的結果也證實了演算法硬體能夠有符合預期的訊號處理效果。

關鍵字

連續侷限型波茲曼演算法 ； 電子鼻

並列摘要

Many portable or implantable microsystems have incorporated sensor arrays for various biomedical applications. The raw sensory signals are usually high-dimensional, noisy, and drifting. To facilitate in-situ diagnosis or to reduce the data for wireless transmission, a low-power, embedded system is demanded for fusing the sensory signals robustly in real time. A probabilistic neural network called the Continuous Restricted Boltzmann Machine (CRBM) has been shown capable of classifying biomedical data reliably. Thus, it is suitable for CRBM to act as a signal pre-processing unit in system.
This paper discuss about how to use CRBM to process sensory data of electronic nose system. At first, it makes pilot simulation in software to confirm the capability of CRBM for processing sensory data. Then it will study the method of implementing CRBM into VLSI (Very Large Scale Integration) and integrating with electronic nose system. The chip of CRBM integrating with electronic nose system has been designed and fabricated with the TSMC 0.18μm and 90nm technology provided by TSMC (Taiwan Semiconductor Manufacturing Company). The measurement results proved that the CRBM hardware system can perform good processed results as expected.

並列關鍵字

CRBM ； eNose

參考文獻

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