本論文提出一個基於演算法暨架構共同探索之方法之應用於心電圖賈伯特徵萃取演算法的一維賈伯濾波器組資料流模型設計。傳統的設計方法只根據時間複雜度評估演算法忽略了演算法與架構之間的關係，造成演算法與架構設計的隔閡；相較之下演算法暨架構共同設計透過資料流模型配合多抽象層級以及多資料粒度分析對演算法與架構間共同評估運算複雜度、可平行化程度、資料儲存需求與資料傳輸量。由於資料流模型當做演算法與架構的橋梁，提出的資料流模型透過賈伯對稱性達成乘法折減、分析共通性達成加法折減與透過重定時折減關鍵路徑；較相關文獻比較更具效能、驗證於模擬資料傳遞的高階程式設計語言CAL。

This paper presents a dataflow design of 1-D Gabor Wavelet Transform based on an Algorithm/Architecture Co-exploration (AAC) design methodology for an electrocardiogram (ECG) Gabor feature extraction system. Traditional design methodologies only focus on execution time which ignores the relationship between algorithm and architecture design; on the contrary, AAC considers overall complexity between algorithm and architecture, such as number of operations, data storage, data transfer and degree of parallelism, and these complexity can be extracted by dataflow with multiple abstract levels and various data granularities. Due to dataflow exploration as a bridge between algorithm and architecture, proposed dataflow achieve multiplication reduction with Gabor symmetry property, addition reduction with commonality, and critical path reduction with retiming; more efficient than related works, and verified by CAL, which is a high-level programming language for writing dataflow to model data-driven processing.

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