Intel AIDC 開發大會主題演講,以軟體開源、多元硬體與社群作為 AI 戰略後盾

2018.05.24 03:47AM
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對於現在電子產業而言,由於硬體運算力提升、運算軟體框架不斷進化且市場有明確需求,基於神經網路的 AI 再度成為重要議題,而作為現行全球主要伺服器、超級電腦核心架構供應商的 Intel 當然也不會錯過這場盛會,鎖定 AI 相關開發者舉辦了 AIDC ( AI 開發者大會),在稍早的主題演講,也就 Intel 目前的戰略進行說明。

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Intel 的 AI 戰略可分為三大領域,包括軟體、硬體與社群;其中在社群方面, Intel 選擇藉由開源方式推廣相關開發軟體,而硬體部分除了 Intel 最核心的 Xeon 外,也針對當前異構運算、推理與學習加速器等提供全方位的硬體解決方案,至於社群除了與業界主力的 AI 框架如 Google微軟與 Facebook 持續合作外,也藉由 AIDC 拋磚引玉,接下來也將在全球陸續舉辦開發者大會。

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人工智慧相關軟體, Intel 現在提供多元的深度學習相關軟體並在 GitHub 進行開源,首先就是 Intel Math Kernel Library ,藉由推出這項軟體工具並且與 Google 的 TensorFlow ,能夠大幅提升 Intel 處理器執行 TensorFlow 的效率,此外也將整合多樣主流框架相容性的 nGRAPH 、分散式深度學習資料庫的 BigDL 等進行開源,另外甫宣布針對邊際裝置的視覺運算的 OpenVINO 也預計在今年底前進行開源。

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至於硬體戰略部分, Intel 強調 Xeon 仍是作為目前世界上多數人工智慧運算的核心部分,同時藉由每一世代的硬體架構、演算法的提升,效能仍持續提升中,同時也藉由支援大量的記憶體,使得深度學習允許更巨量的資料進行處理,畢竟作為深度學習培訓,記憶體的容量影響可處理的資料量。

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當然作為當前深度學習的硬體架構,純 CPU 運算漸漸受到異構運算的挑戰,尤其是 GPU 運算更是市場的主力,不過 Intel 雖認為藉由 GPU 與 CPU 是相當不錯的 AI 架構,但仍有更具效率的做法, Intel 也提供多項解決方案為人工智慧提供更具效率的架構,包括 FPGA 、神經網路加速器以及視覺運算處理器等。

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Intel 認為, FPGA 相較 GPU 具備更高的架構彈性,可依照人工智慧的運算項需求,藉可程式化特性調整 FP16 、 FP32 與 FP11 等的算力,較 GPU 甚至可提供達 7 倍的效能,也同時提供更好的能源效率。

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而視覺加速器解決方案 Movidius 則藉由針對視覺運算的硬體加速架構,為邊際運算裝置預先處理影像相關的內容,使其背後作為人工智慧的算力不用花費在這些影像相關的處理上,另外除了視覺加速外,亦能作為音樂的頻譜分析,會場也藉由 Movidius 分析電子琴輸入的音節後,再以電吉他聲互動。

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至於 Nervana 則是 Intel 針對類神經網路所打造的類神經網路處理器,意義上與 Google 的 NPU 類似,具備達 2.4TB/s 的相互溝通通道,能連接多個 Nervana 並提供更高效能;而 Intel 強調 2019 年將推出商用版的第二世代 Nervana " NNP L-1000 ",效能將較現行第一世代提升 3-4 倍。

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最後提到 Intel 的夥伴關係,除了與資料中心、人工智慧框架供應商維持良好的關係外,同時也與包括 CG 產業、運動賽事等進行合作,藉由人工智慧帶來如更快速的 CG 影像特效建構,或是在如北美法拉力挑戰賽帶來更好的賽事轉播,同時除了先前贊助平昌奧運外, Intel 也將在繼續贊助 2020 冬季奧運,藉此展現如 5G 技術、人工智慧分析與轉播等技術。

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