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Technology Development Directions for Taiwan’s AI Industry

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講者:
工研院資通所闕志克所長

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Technology Development Directions for Taiwan’s AI Industry

  1. 1. Technology Development Directions for Taiwan’s AI Industry Tzi-cker Chiueh 闕志克 Information and Communications Labs
  2. 2. Introduction to AI System • AI ~ Deep Neural Network (DNN)-based Machine Learning • AI system: a system with analysis and synthesis capabilities powered by DNN- based machine learning – Autonomous driving vehicle, drone, robot, personal virtual assistant, etc. • Machine learning: a universal algorithm for building a functional mapping between sample inputs and associated outputs – A new paradigm of software development – Learn from many normal people vs. Design with few gifted experts • From AI Winter to AI Everywhere – Algorithmic breakthrough that enables training of deep neural network – Large high-quality training data set, e.g., ImageNet – Availability of high-performance GPU
  3. 3. Overarching Strategies • 產業AI化: Apply modern AI techniques to improving the value and efficiency of existing industry segments, – As common a tool as MatLab – Medicine: diagnostics, nursing care – Manufacturing: defect detection, equipment maintenance, robotic manipulation – Finance: credit assessment, personal investment, trading algorithm – Commerce: advertisement, retail analytics, logistics planning • AI產業化: Convert modern AI techniques into new systems and products that enable applications of AI – High-performance DNN training – Real-time low-power DNN inferencing – DNN-based systems: autonomous driving vehicle, autonomous drone, robot, personal virtual assistant
  4. 4. Machine Learning Basics • Supervised Learning: from sample input-output pairs – Labeling a training data set  knowledge acquisition – Training to get a functional model  knowledge transfer & abstraction – Applying a learned model  knowledge application – Ask the right question: Set up a proper optimization objective function: “Like this?” – Training corresponds to multi-variable non-linear optimization • Universal Approximation Theorem • Gradient descent-based search • Unsupervised Learning – Clustering – Factor analysis – Auto encoding
  5. 5. Training and Inference of Neural Network Training: Forward/Backward Propagation  Inference: Forward Propagation 
  6. 6. Key Technical Challenges in DNN • Training of DNN model – Quality: how to acquire high-quality training data set • Label correctness and diversity • Semi-automatic training data collection and labeling – Speed: • Reduce the number of rounds required in the training process – Round  Epoch  Batch • Reduce the computation overhead associated with each training round • Speed and power consumption of applying DNN model (inference) – Real-time: autonomous driving – Embedded system: low power and low cost • Explainability of learned DNN models • Broadening the scope of DNN applications: from analysis to synthesis
  7. 7. Overview of DNN Systems Research • Computational challenges brought by DNN – Training (off-line) • DNN Integrated Development Environment (IDE): reduce the number of iterations required in a training process • DNN Training Appliance: reduce the amount of time taken by each training iteration, which involves multiple passes through the training data set, e.g., Nvidia’s DGX-1 and Intel/Nervana’s Lake Crest – Inference (on-line) • Cloud-based DNN inference engine (high performance), e.g., Google’s TensorFlow processing unit (TPU) • Embedded DNN inference engine (low power and real-time) for smartphone, and automatic driving vehicle, e.g., Nvidia’s TX2, Intel’s Movidius (Neural Computing Stick), and Mobileye’s EyeQ
  8. 8. DNN Training Appliance • DNN training has emerged as a crucial class of workloads in future data centers • DNN appliance: a system that integrates (I) DNN IDE, (II) DNN model optimization, (III) DNN training computation mapping and scheduling, and (IV) GPU-based compute cluster to minimize the end-to-end training time • Nvidia’s DGX-1 – Deep learning supercomputer – NTD $4M + $1M – P100 GPU + NVlink – 170 TeraFLOPs of FP16 – Effective performance equal to 250 Intel x86 CPU-based servers – HGX-1: Hyperscale GPU Accelerator
  9. 9. ITRI DNN Training Appliance • Objective: Enable Taiwan to become a major power of DNN training appliances • Hardware enhancements – Processor • Nvidia’s Tesla P100 and V100 (12GB, 4.7TFLOPs, $5899) • Nvidia’s GeForce GTX 1080Ti (11GB, 11.3TFLOPs of FP32, $699) • AMD’s Radeon RX-500 and RX Vega • Intel’s Knights Mill (KNM) – System Interconnect • NVlink (Gen-Z) • Meshed PCIe network – Cooling • Software Optimizations – Minimize the performance impacts of lack of NVlink – DNN training integrated development environment • Leverage MOST’s AI computation platform as a reference case Graphics driver API: • CUDA • OpenCL
  10. 10. DNN Inference Engine • Track 1: Sensor data processing platform for autonomous driving – Distributed architecture: edge processing and leaner network – Centralized architecture: fatter network and more efficient processing resource utilization • Track 2: Customized DNN inference processor design – Digital hardware approach • Make data access as efficient as possible • Decrease the total amount of computation for every possible input • Reduce the amount of computation for easy or already seen inputs – Analog hardware approach • Programmable and persistent resistors • I (output) = V (input) * 1/R could be used to implement multiplication • Wired-OR implements (current) addition
  11. 11. 無人駕駛車產業技術
  12. 12. SAE (Society of Automotive Engineering) *L3補充說明:自動駕駛系統在發生故障或者超過使用範圍,需要沒辦法直接退出系 統功能,需要給車主足夠的時間來看,準備好方向盤和剎車,系統不需要達到最小 化風險 SAE level 方向/加速/減 速動作執行 駕駛環境 的掌控感知 駕駛任務動態 接手* 行車環境 類型 0 人 人 人 1 人+系統 人 人 部分 2 系統 人 人 部分 3 系統 系統 人 部分 4 系統 系統 系統 部分 5 系統 系統 系統 全部 資料來源: 自動駕駛的等級
  13. 13. Autonomous Driving Vehicle (ADV) 13 Image Sensor LiDAR Radar 感測分析硬體 深度學習影像辨識 多重感測融合 自動駕駛事件推理 自動駕駛決策 模組 人機介面次 系統 生成/修正目標行駛 路線 動力系統制動 車速控制模組 方向盤操控控 制模組 感知次系統 智慧決策 整合控制 定位與地圖 /交通資訊與動態路 徑規畫模組 聚焦發展感知次系統,為車用 電子能量延伸發展之領域 由車廠主導 由車廠主導
  14. 14. Key Strategic Decisions 以次系統為優先開發主軸 特定場域自動接駁服務 開發期系統測試能力 高延展性DNN技術自主  先投入感知次系統掌 握關鍵  後結合控制與決策次 系統鞏固市場  開發特定場域自駕車  特定場域自駕車接駁 服務  感知技術以DNN為主, 強化大規模訓練所需的 資料及方法  聚焦東亞/東南亞地區 行車資料蒐集  測試、驗證、展示、試 營運  沙崙場域、工研院中興 院區、新竹高鐵等場域
  15. 15. 15 Technology Development Plan 通用移動電訊、趨勢科技、聯 發科、新唐、華創車電 3.自駕車規之 資安及可靠度 1. 泛用車輛環境感 知次系統 • 我國首套自主研發之自動 接駁系統,民眾可透過 APP定點呼叫自駕車(預計 108年可定點接駁) 2. 特定場域自駕 車接駁服務 宏碁、華創、車王電、 利佳、光寶、明泰 • 具備自駕車行駛對周遭任何物件可 即時感知相對位置、速度、近端軌 跡,並具備事件推理能力 • 目標:與Mobileye in 2021並駕齊驅 • 協助我國廠商建立自主 「快速反應、高可靠與 高安全性」自駕車資安 系統 自動駕駛感知次系統技術 深度學習影像辨識 聯發科、明泰、朋程、瑞科、 光寶、研勤、勤崴、為升、鼎 天 多重感測融合 即時事件推理 感測分析硬體 自動 駕駛 感知 次系 統 感知次系統驗證
  16. 16. 16 DNN Training Data Collection for Taiwan’s Road Conditions  發展策略: • 以深度學習為核心研發適用於各種道路環境之影像辨識技術,補足我國相關產業與國際車 用大廠(如Mobileye)間之競爭基礎。針對台灣各種道路環境與天候狀況建立亞洲第一套以 自動駕駛訓練所用台灣街景影像資料庫「FORMOSA」,利用既有計畫資源(如車隊裝設 影像設備)迅速蒐集訓練資料降低成本,並累積至少10萬公里的影像訓練資料內容,厚實 國內相關產業發展即時影像辨識技術之基礎。  執行重點: • 真實環境視訊物件與事件之蒐集 • 晴天、陰天、雨天環境等 • 車輛物件: 大客車、小客車、貨車、聯結車、露營車等 • 交通號誌物件:速限號誌、紅綠燈、交通標誌、道路路面、落地招牌、道路標線等 • 多種感測器:光達(LiDAR)、雷達(RADAR)等 • 動態事件:行人、四輪車輛、二輪車輛與交通號誌、紅綠燈狀態變化等。 • 視訊資料標記工具 • 台灣街景影像資料庫「 FORMOSA 」之資料格式特徵與物件類別定義,並進行人工 標記作業與開發半自動化影像物件標記工具,以簡化人工標記成本。
  17. 17. 17 特定場域情境自駕車 系統開發時程 特定場域自駕車接駁服務 *
  18. 18. 無人機應用服務系統
  19. 19. 19Copyright 2017 限閱資料、禁止複製、轉載及外流 具台灣地域特色之無人機應用  台灣特色下無人機可擔任之角色與加值開發方向 資料來源:MIC (2017/08) 台灣空域或 環境特色 台灣特色(簡述) 適合無人機技術發展或輸出方案 (舉例) 人口密度 台灣人口密度,全球排名為九。 現場人數統計、民眾移動流量、移動追蹤,但也為使用無機最大安全 顧忌之一。 橋梁密度 全台10,769座公路橋梁,1,605座鐵道 橋梁 巡檢橋梁、裂縫檢視、河川水位與流量、建物靜載重與變形監視、其 他公共設施與建物巡檢維護。 河川密度 短小、流速快、流量變化大,具116獨 立水系。 污染源巡視、水源地保護、高灘地管理、氾濫或乾涸, 複合災害 颱風多、地震多,帶來複合型的災害。 災區掌握、指揮調度、搜尋搜救、協助災區通訊、復原災區緊急物質 運補。 生態物種 熱帶、亞熱帶、溫帶及寒帶各氣候類 型均有,鳥種密度世界第二。 生態觀測、植被檢視,生態資源盤點、水土保持巡檢、看見台灣空拍、 台灣3D景致觀賞、3D型式觀光服務。 小農密度 農牧戶人口為301萬餘人,占總人口數 12.9%。 精準農業、農產估算、噴藥施肥、巡田檢視、作物生病蟲害觀測。 違章建築 全台違章建築未拆除數量高達67.3萬 件,違章建築未拆除數量續創新高。 違章建築查報、違章建築統計、地政分析監理。 海岸線長 北部為岬角海岸、東部為斷層海岸、 南部為珊瑚礁海岸、西部為沙質海岸。 國土巡邏、海岸監理巡防、遏止偷渡運毒、看見台灣無動力滑翔機/飛 行傘活動或觀光。 高低起伏 地形豐富,高低變化大。短距離切面 即有海岸、河谷、平原、丘陵、台地 與山地。具268座3千公尺以上的高山。 植被生態觀測養護、國家森林資源盤保護、水土保持。 全球無人機產品與應用最佳試飛與驗證場地。 富饒3D景致表現、觀光活動或科研活動、無人機競爭。 無人區 人口集中西半部平原,中央山脈與東 部人稀少,僅少部分平原有人口分布。 礦產或森林資源探勘、生態觀測植被檢視;不用造橋鋪路,無人機能 入無人之區巡視與運補;提供無人機安全航道或試飛空域有更多選項。
  20. 20. 20Copyright 2017 限閱資料、禁止複製、轉載及外流 無人機產業鏈 無人機製造/品牌商 酬載系統 (Payload) 雲台 (Gimbal)、 攝影機、熱像儀等 飛控系統 飛控軟體、陀螺儀 、磁力計等 機體元件 馬達、電池、 旋翼、機架等 通訊模組 GPS、RC/ 4G通訊元件等 操作系統 遙控器、 地面站軟體 無人機隊應用服務維運商 c 學研關鍵技術 應用軟體商 運研所:交通流量分析 高鐵局:橋梁檢測 水利署:砂石盜採、水庫巡檢 能源局:太陽能/風力/電力檢測 警政署:維安、偏鄉巡邏 消防署:災害救援 內政 經濟 交通 空氣、河川、坡 地及廢棄物監測 環保 農藥噴灑、魚塭 巡檢、精準農業 農業 保險業者無人機系統整合商 資料來源:本計畫整理(2017/08)
  21. 21. 21Copyright 2017 限閱資料、禁止複製、轉載及外流 無人機應用核心技術開發/實證計畫  基於國際無人機發展趨勢、我國資通訊產業優勢及無人機產業缺口,發展高價 值無人機隊應用核心技術與系統服務,帶動軟硬體及系統整合產業發展。  建立穩定、安全、長時、遠距及智慧化之無人機隊系統,結合國內警政巡邏、 智慧巡檢(橋檢/環保)、救災通訊等應用場域進行驗證,提供民眾有感服務。 資料來源:本計畫整理(2017/08) 無人機核心軟體技術 • 無人機安全飛控技術 • 三維避障防撞技術 執勤時間展延技術 • 自動無人機充電站技術 • 長效動力管理技術 • 節能飛行管理技術 酬載系統技術 • 無人機酬載介面標準化 • 酬載協同操控與顯示技術 無人機隊管理技術 • 多機編隊遠距執勤技術 • 無人機空域管理系統 場域實證推動 • 警用巡邏無人機技術 • 智慧巡檢無人機技術 • 救災通訊無人機技術 警政雲 無縫結合路口監視、M-Police及 無人機影音,建構更安全之生活 環境 提升安全性 介面標準化 服務自動化增加續航力 垂直應用整合 警用巡邏 智慧巡檢 救災通訊
  22. 22. 22Copyright 2017 限閱資料、禁止複製、轉載及外流 環保空污偵測  桃園環保局/觀音工業區:環保空污偵測方案 每2秒sample一次 飛行速度:5m/s 三級不同高度的飛行計畫 Mission 1 Mission 2 Mission 3
  23. 23. 23Copyright 2017 限閱資料、禁止複製、轉載及外流 高鐵/國道橋梁巡檢  高鐵:台中大甲溪/新竹頭前溪段,進行電力桿/支承墊/橋墩等檢測。  高公局:國道六號橋聳雲天(平均高度58公尺),進行支承墊/橋面/鋼橋檢測。 台中大甲溪 電力桿 螺栓鬆脫 混泥土剝落 鋼橋檢視國六支承墊檢視 國道六號橋聳雲天 飛行路線& Way Point Way Point 行為設定 30m 無人機飛行排程 排程路徑拍攝現有橋檢車作業
  24. 24. 24Copyright 2017 限閱資料、禁止複製、轉載及外流 太陽能板巡檢  沙崙場域/工研院南分院:進行太陽能板排程巡檢驗證。 沙崙綠能科學城 無人機測試場域建置規劃
  25. 25. 25Copyright 2017 限閱資料、禁止複製、轉載及外流 高壓電塔 礙子清洗 • 台電每年在高壓電塔維護作業花費 > NT$80億。 • 需求如:預防電纜線連接頭因高溫斷裂毀損而斷電、礙子表面汙損時洩漏電流及 清掃、高壓電塔與附近的異狀物的距離觀測、高壓電塔表面銹蝕檢測與補漆。 Manpower Helicopters UAV Man Hour Costs 12.5 thousand/Hour (Annual cost about 7.5 billion a year) 100 thousand / Hour (Annual cost about 0.5 billion) 10 thousand / Hour Working Area City / Reachable place Remote /Rural Areas (Height limit in recent years) No Limit Safety Electric shock / Person fall down Mechanical failure / Turbulence (There are accidents these three years) No Casualty Cleaning Method Short Distance Cloth wiping / Water Column Long Distance High pressure water column cleaning Short Distance Dry Ice Cleaning Real-Time Surveillance System Operation Power Outage / Live Line Live Line Live Line Manpower : high pressure water column cleaningManpower : Cloth wiping UAV dry ice cleaning Helicopters: high pressure water column cleaning
  26. 26. 自動資安攻防系統
  27. 27. Common Cyber Attack Scenarios • From the Internet – Scanning public IP addresses – Fingerprinting the OS and applications – Applying attacks to exploit known vulnerabilities – Increasingly difficult with multi-level defense around servers • From the intranet – Social engineering via email or social network sites – Drive-by download – Stepping stone to attack enterprises – Increasingly common with dirtier endpoints 27
  28. 28. Exploiting a Vulnerability Network-Facing Application Malware (3) Bootstrap Payload (2) Cause Damage RUN (4) Vulnerability (1) 28
  29. 29. Programmatic Defense Mechanisms • Stop injection of shell code (Step 1) – Eliminate security loopholes in applications – Buffer overflow prevention: CASH: a fast bounds checking compiler • Stop injected shell code from performing unauthorized sensitive operations (Step 2) – Address space or library randomization – System call monitoring: PAID: an accurate system call monitoring tool • Stop when malicious binaries are downloaded (Step 3) – Scanning of malware during transit in firewalls • Stop an injected malware at invocation time (Step 4) – Black-listing: traditional anti-virus SW – White-listing – Run-time behavior monitoring29
  30. 30. DARPA’s Cyber Grand Challenge • Problem: Once a bug is announced, a race is on between bad guys that aim to exploit the bug, and good guys that aim to patch the bug, and bad guys typically win. • Goal: DARPA seeks to create automatic defensive systems that are capable of reasoning about flaws, formulating patches and deploying them on a network in real time. • Result: Seven teams competed in August 2016, and the champion team, ForAllSecure, led by David Brumley from CMU, took home a $2 million award.30
  31. 31. Automated Cyber Defense/Offense Program analysis-based software security – Input: Adobe flash player has a bug – Outputs: • Where is the bug? Is it a vulnerability? • How to exploit it? • How to patch it? • How to develop an intrusion detection signature for it? – Exploration approaches • Fuzzing: random mutation, and anomaly- or corner case-driven stressing • Intelligent fuzzing: exploiting knowledge on inputs • Symbolic execution : source code/binary code – Full automation: Bug  Vulnerability  Patch  Signature  Attack – Targets: DARPA CGC and NSA/CIA’s leaked attack toolkit31
  32. 32. Summary • DNN is expected to become the focal point of ICT research in the next couple of years – Myriad applications are possible and promising • Analysis and Synthesis • More promising bet is on business/enterprise applications using natural language understanding, e.g., legal technology, regulatory technology, patent analysis, etc. – Systems support for DNN training and inference is a promising area • DNN training appliance and DNN inference processor • Competition in DNN inference processor is like that in GPU design 15 years ago. • DNN-based systems – Perception subsystem for autonomous driving – Tele-operated drone-based application services – Automated cyber defense and offense
  33. 33. Thank You! Questions and Comments? tcc@itri.org.tw
  34. 34. Training Data Collection and Labeling • ImageNet is the most successful example so far • How to reduce the effort and improve the quality of training data collection and labeling? – Crowd sourcing for raw data collection – Human-based computation: image labeling as a multi-user game – Computer-aided labeling: object/label tracking across video frames – Generation of meaningful new training data from example data set – Unsupervised learning • Corner-case training data collection and labeling – Crowd sourcing-based collection – Model-driven synthesis (for ADV) • Augmented reality: videos augmented with graphics objects • Photo-realistic graphics rendering
  35. 35. Analysis Applications of DNN • Perception subsystem for Autonomous Driving – The relative coordinate, relative speed and future trajectory of every driving-related object within a certain distance – Real-time video object locationing, recognition and tracking: YOLO  object detection and classification in one shot – Driving event prediction – Multi-sensor data fusion and analysis: RGBD • Analytics for New Retail – Trajectory of every shopper, the set of merchandises he touches, and his likings
  36. 36. Synthesis Applications of DNN • Movie critic  Movie director • Generative Adversarial Network (GAN) : a game theoretic approach to converting an analysis model into a synthesis model

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