本論文旨在評估通用型脈動陣列 AI 硬體加速器在不同類型神經網路模型上的適用性及效能。隨著深度學習在邊緣運算中的廣泛應用，硬體加速器的設計成為提升邊緣運算效率的關鍵。然而，為每種類神經網路配置專用的硬體加速器並不切實際，若硬體加速器配置需要隨著模型架構的不同而頻繁改變，將是高昂成本負擔。
本論文提出一套通用型 AI 脈動陣列硬體加速器的配置，目的是解決類神經網路應用中硬體適配的問題，使單一硬體加速器能夠適用於多種不同類神經網路架構，並建立了一個基於 RISC-V 核心且與通用型 AI 硬體加速器做整合之SoC 架構平台，實作於 FPGA 板，該 SoC架構提供一個真實情況的評估平台。
本論文選用 Gemmini 作為通用型脈動陣列 AI 硬體加速器的代表，在不同的硬體配置下，針對兩種具代表性的類神經網路模型進行實驗，分別是基於二維卷積神經網路的影像元件辨識模型以及基於一維卷積的手勢辨識模型。本研究會結合效能評估並衡量 FPGA 硬體資源使用量，提出合適的通用型脈動陣列加速器硬體配置選用方案，供 AI 領域研究者參考。

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