應用於生醫訊號之可程式化積卷式長短期記憶類神經網絡特殊應用晶片設計

行動醫療裝置是健康照護的重要關鍵。行動醫療裝置能夠及時地分析病患或一般民眾的健康狀況，讓使用者能隨時掌控自己的身體狀況。行動醫療結合了人工智慧、大數據分析、物聯網、感應器等多項科技，是個人健康照護中相當重要的環節。行動醫療裝置不像雲端運算有著強大的運算能力及不受限制的電力來源，行動醫療裝置必須實現邊緣運算。而邊緣運算有兩大重點：低功耗、實時運算。邊緣運算是利用電池供給電力的，電力相當受限，且大功率消耗所產生的廢熱也會讓使用者感到不適；不同於雲端運算能夠容許資料傳輸的延遲，行動醫療裝置必須實時地在本地端完成運算，提供使用者即時的分析結果。為了達到低功耗實時的邊緣運算，我們設計了能夠支援多種深度學習架構的特殊應用晶片。深度學習的模型包含CNN、LSTM、FC等三種模型，並且加入可程式化之特性，使電路能夠運行不同層數，不同內核數的CNN、LSTM。藉由共用單一處理單元的方式以及內核暫存器共用的方式來達到低功耗。在實時處理的限制下，電路模擬的動態功耗為2.56 uW, 靜態功耗為224 uW，總功耗為226.56 uW，由於電路運行速度不快，操作頻率在3MHz，絕大多數功耗為靜態功耗。我們處理的生醫訊號以PPG訊號為主，主要的應用為身分辨識器，訊號挑選器，以及血糖估測器。前兩種應用使用了LSTM+FC，而且血糖估測則使用了CNN+LSTM+FC。此三種應用結合在行動醫療裝置中能帶來更安全，且更穩定的效果。

關鍵字

人工智慧；卷積神經網路；長短期記憶模型；特殊應用晶片；生物醫學；行動醫療

並列摘要

Mobile health device is key factor for personal health care. Mobile health device can analyze user's physical well-being instantly. Mobile health device combines Artificial Intelligence, Big Data, Internet of Things, sensors, etc. It plays important role in personal health care. Unlike cloud computing, mobile health device has very limited computing resources and computing power, mobile health device needs to achieve edge computing. Edge computing requires low power and real-time computing. To achieve low power and real-time computing, we design an ASIC that can process multiple deep learning networks. Supported deep learning networks includes Convolutional Neural Network, Long Short Term Memory and Fully Connect. We also make the ASIC programmable, so that our ASIC can support different layers, kernel sizes, channel sizes for CNN and LSTM. Our ASIC achieve low power by sharing same PE among all three networks, and the main buffers used by LSTM is fully shared with FC. Under the real-time processing constrain, our ASIC can achieve 2.56 uW dynamic power and 224 uW static power, the total power is only 226.56 uW, since our ASIC is not running very fast, the clock frequency is only 3MHz, so most of power consumption is from static power. Our ASIC mainly processes PPG signal, and main application is Biometric Identification, Signal Selector, and Blood Glucose Predictor. The first two application utilize LSTM and FC networks. Blood Glucose Predictor utilize CNN, LSTM and FC. By combining these three networks, we can offer more stable and secure personal health care.