硬科技:科科們來瞧瞧一窩蜂猛衝人工智慧的勇者們

2018.10.29 12:59PM
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近年來工人智慧... 呃,將類神經網路換成幾個新潮名詞的人工智慧正夯,結合自動駕駛這股看似可以永遠消滅馬路三寶的熱潮,不分業界廠商或學術機構,清一色爭先恐後的涉足這個早不新奇的領域 ,近年來的IEEE HotChips的議程標題,剛剛好完美呈現這股「趨勢」,這2年佔了將近一半的比例,如計入HotChips 30在議程以外的技術教程 (Tutorial)、主題演講 (Keynote) 和宣傳海報 (Poster) 就更誇張了。如同IEEE ISCA ( International Symposium on Computer Architecture,國際計算機架構會議) 從1990年代以降,因為關於Cache的論文不計其數,所以被戲稱「國際Cache架構研討會」,假以時日,該不會哪天HotChips就被暱稱成「AI Chips」吧?

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參考文章:硬科技:一窺Google TPU全貌 見證雲端霸主在AI的發展

各位科科也可以從議程主題的發表順序、上台頻率與所屬產業,看出不同廠商之間「格局」的差異。以去年HotChips 29來說,最後一場壓軸好戲:一向喜歡鴨子划水的Google一口氣揭露2個世代TPU的技術細節,彷彿氣勢萬鈞的昭告天下「你們這些比我晚好幾年才後知後覺的呆瓜們都可以回家洗洗睡啦,全世界直接用我的人工智慧雲端服務就夠了」,但這依舊無法阻擋前仆後繼、意圖瓜分一席之地的勇者們,連FPGA廠商都跳進來加入戰局了,好不熱鬧。

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看著空前「盛況」看到極度無言的筆者,就依序簡單介紹今年HotChips 30人工智慧的相關議程。

Google Pixel 2 / 3手機的Visual Pixel Core

參考文章:拍攝影像更美麗秘訣 Google透露Pixel 2內整合全新影像處理元件Pixel 3 XL拆解確認沿用Pixel 2的VPU視覺運算元件 改用三星AMOLED顯示面板

好吧,看來除非第二代VPU早早問世,明年Google在HotChips 31大概就少了一個可講的題材了。等一下,第三代TPU跑到哪裡去了?

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適用IoT裝置的「深度學習加速器」SoC

僅僅7人用9個月的時間就完成設計,還內建FPGA,看起來有點威,這真的是無晶圓廠IC設計公司商業模式的大勝利啊,可是對IoT裝置來說,這晶片封裝會不會太大了?

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NVIDIA將在今年初開始供應新一代Level 3 ~ Level 4自動駕駛晶片「Xavier」

參考文章:硬科技:NVIDIA車駕晶片Xavier最大的祕密:初探「Carmel」微架構

NVIDIA竟然膽敢把「飛行車」也列入Xavier的守備範圍,可是當飛行車可以普及,NVIDIA的車駕晶片都不知道演進多少世代了。

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將深度神經網路的乘加運算做在快閃記憶體內

直接在記憶體內執行運算、無須將資料移出移入的記憶體式運算架構 (In-Memory Computing,或稱Computational Memory以及Processor-In-Memory) ,一直都是被長期研究的技術,而Mythic這間新創公司,則企圖在NOR快閃記憶體陣列內進行神經網路運算工作,不僅可提昇效能,並藉由快閃記憶體的不可揮發性 (斷電也能保存資料) 而大幅節省功耗。這議程的內容真的很猛,記憶體內運算模式和新世代非揮發性記憶體的結合,絕對是值得各位科科關注的重大趨勢。

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總算推出機械學習解決方案IP的Arm

最近一兩年Arm「日子看起來沒有那麼好過」的主因很簡單:來不及針對新興應用,例如軟體定義網路 (SDN)、挖礦、甚至人工智慧,提出解決方案的IP,結果變成Apple、Qualcomm、Samsung華為海思、聯發科等廠商,都自行開發用於機器學習的硬體「神經網路引擎」,並陸續整合於自家的手機處理器,等於Arm根本吃不到這一塊。現在隨著第一世代機械學習處理器IP的問世,Arm總算終結了「人工智慧旱災」,但之後會有多少晶片廠商願意買單就不得而知了。

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NVIDIA「Xavier」內建的深度學習加速器

參考文章:硬科技:一窺NVIDIA「真正人工智慧」Volta的執行單元細節

NVIDIA繼續冷飯熱炒,不過他們這次有開源RTL並燒錄至FPGA與SiFive的RISC-V SoC整合在一起,算是一個亮點,有利於NVIDIA日後的推廣工作。

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滿足多樣化應用的低功耗雲端資料中心處理器

這應該是筆者看過口氣最大的技術簡報。

Tachyum這間公司自行設計了全新「類」VLIW指令集 (看起來很近似Intel IA-64) 與相對應的「業界第一個面面俱到的汎用處理器 (The Industry’s First Universal Processor)」,宣稱比現有CPU、GPU、TPU還要好,並且「比Xeon快,比Arm還小」,同時為了伺服器、高效能運算和人工智慧最佳化。

嗯,就算以上說詞都不是空頭支票好了,還是得先看看有沒有足夠的可用軟體和有多少膽敢導入的客戶吧,靜觀其變,以不變應萬變。

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中國色彩的FPGA深度學習推論處理器

DeePhi (深鑒科技) 是一間成立於中國北京的機器學習解決方案新創公司,據傳是Samsung Exynos 9810內部深度學習加速器的技術來源,其語音辨識引擎也同時被Amazon和華為採用。從2016年創設以來,DeePhi一直使用Xilinx的FPGA開發產品。2017年,Xilinx先投資這間公司,後來在2018年就直接併購以「掌握業界難覓的深度學習技術人才」,但這只是Xilinx人工智慧布局的一小部份。

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Xilinx自家的FPGA深度學習處理器方案

參考文章:硬科技:一窺Google TPU全貌 見證雲端霸主在AI的發展

不只在三年多前以167億美元併購FPGA第二大廠Altera的Intel (2017年HotChips 29,微軟的「Project BrainWave」就是採用Intel的FPGA),身為第一大廠的Xilinx也積極投入將FPGA應用於人工智慧的「整體解決方案」。

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今日的高階FPGA處理器也是規格強的誇張,連HBM都有,在筆者首次得知什麼是FPGA的年代實在是令人難以想像,如果價格可以更便宜一點就更棒了。相對於燒錢自力打造人工智慧專用ASIC (TPU) 的Google,FPGA的確是「先講求不傷荷包,再講求效果」的保險方案。嗯,連Systolic Array (脈動陣列) 都出現了,乍看之下還以為是Google的TPU。

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明年應該還是會一樣的熱鬧非凡

依照目前的勢頭,來自不同領域的各方人馬,持續進軍人工智慧的狂熱,明年依然會方興未艾,各位科科恐怕還是會繼續看眾多廠商的精彩演出看到猛翻白眼。但即使技術看起來很厲害是一回事,能不能在日常生活感受到被新技術所改善的生活品質才是重點。最讓人擔心的,莫過於人工智慧被不對的人應用到錯誤的方向,搞不好使人未蒙其利先受其害。真的是越想越感到毛骨悚然啊,還是不要想下去好了。科科。

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