隨著半導體製程的演進與晶片製作技術的進步，現今已能在較小的晶片體積中實現高複雜度之資料處理與運算，異構多核心系統(HMS)已被廣泛利用，透過將適當的工作內容與執行核心進行匹配處理，將能更有效率地進行資料運算與處理。同時，老化效應(Aging effect)對晶片的可靠度造成了嚴重威脅，其中負偏壓溫度不穩定性(NBTI)將會隨著晶片的運作，逐漸提高P型電晶的閾值電壓(threshold voltage)，使得晶片在使用一段時間後，其訊號傳遞延遲將有可能大於設計時所制定之規格，進而造成訊號之時序錯誤，而影響晶片之可靠度。為了避免此一現象，偵測NBTI造成之訊號傳遞延遲方法及減緩NBTI之設計與優化等方法陸續被提出，然而卻少有文獻著重於NBTI對HMS統造成的影響進行較深入的探討。因此在此計畫中，我們將深入探討HNS在執行不同工作內容的情況下，NBTI對不同模組造成的老化影響，透過機器學習進行晶片老化狀態偵測與偵測結果校準，並透過此結果，在系統層級進行延緩老化策略開發。具體而言，本研究將解決下列兩大困難問題：1.利用機器學習演算法進行晶片模組中老化感測器之布局及結果校準2.考量負偏壓溫度不穩定效應之異構多核心系統生命週期延長策略 我們將利用機器學習演算法，先進行老化感測器於晶片模組中之布局，再開發工作內容與執行核心之匹配演算法，達成延長HMS老化之目標。 ;As CMOS technology continuous scaling down, a single chip can perform complicated data processing. Heterogeneous Multi-core System(HMS) can provide higher performance by appropriately performing task-to-core assignment. On the other hand, aging effect has become one of the most drastic challenges in modern IC design. Negative-Bias Temperature Instability (NBTI) effect can result in increased threshold voltage of pMOS transistors and may lead to timing failure after circuit aging. To mitigate or tolerance NBTI, previous researches developed different design structures as well as optimization strategies. However, only a few studies focus on NBTI-induced problems on HMSs. Therefore, in the proposal, we want to deeply study these NBTI-induced problems on HMSs, and develop a machine learning based algorithm to detect the aging situation of different modules in the HMSs, and propose a system level NBTI mitigation strategy.Specifically, this proposal addresses on the following two problems:1. Using machine learning algorithm to deploy and calibrate aging sensors in different modules of HMSs.2. NBTI-aware HMS system lifetime extension strategyWe will use machine learning algorithm to appropriately deploy aging sensors, and then develop a task-to-core mapping algorithm to extend the HMS system lifetime.
