Advantech AI Solution

Transcription

1 Advantech AI Solution Deep Learning: An Approach to Artificial Intelligence Overview of AI Architecture Deep Learning Architecture Inference System Scenario Applications Solution Offering

2 AI is the science of making machines intelligent, with the ultimate goal of allowing robotics to possess abilities similar to humans. In reality, AI already has a substantial impact on our lives, in ways that improve human health, safety, and productivity. For example, AI planning and speech recognition helps millions of people to access places that would otherwise be unavailable and intelligent video analysis keeps our streets and undergrounds safe. Deep Learning Solution Takes AI to the Next Stage Implementing AI technologies is important for advancing the scope of IIoT. AI technologies are highly tailored to individual tasks and each application requires specialized research and unique construction. Deep Learning, a form of machine learning based on trained datasets, has facilitated advanced pattern recognition in images, video, and objectactivity recognition. Its algorithms can be applied widely to an array of applications that rely on pattern recognition. The Architecture of Deep Learning The ability to simulate human intelligence such as to reason, goal setting, understanding and generating language, and perception and response to sensory inputs, have become benchmarks to evaluate the progress of AI. And with a series of logical rules being derived from various learning models, we are constantly creating an updated knowledge base for AI. We now need devices, called Inference Systems, to apply that logic for different tasks. There are many deep learning methods that focus on AI training, including largescale machine learning (scaling up existing algorithms to work with extremely large data sets), deep learning (associated with machine vision to enable pattern learning, object and activity recognition), reinforcement learning (instead of pattern mining, shifts the focus to decision making, to help AI advance more deeply into executing actions in the real world), and natural language processing (automatic speech recognition). All these deep learning methods advance the possibilities of applying intelligent solutions to the real world problems around us. Training Dataset Training Inference New Data Trained Model

3 3 Key Success Factors for Inference Systems Markets most likely to adapt and implement Artificial Intelligence (AI) technologies include transportation, robotics, healthcare, education, public safety, and security. Each sector faces a variety of AI related challenges so for example, in transportation it is difficult to create safe and reliable hardware for sensing and selection, and in the public safety and security sectors, which rely heavily on AI due to the number of advanced cameras and drones, the same difficulties exist in finding reliable systems to sustain and perform at the highest level in volatile, harsh and critical environments. So called training servers designed for AI create patterns and recognition algorithms but this is only the beginning, the bigger challenge is to apply deep learning techniques into systems that are able to predict and execute these commands, this is where Inference Systems come into play. Compact in size and form factor for flexible deployment Industrialgrade reliability and endurance under extreme conditions Interconnectivity with arrays of Sensors Advantech Inference Systems Advantech, as a global industrial IoT leader, offers groundbreaking Inference Systems with comprehensive hardware, software integration, and customercentric design services to help our customers get up to speed with AI as quickly as possible. Smart City Applications Surveillance Applications Smart Retail Applications Access Control System Application Program Interface (API) Digital Input Digital Output Advantech Deep Learning Inference Systems CCTV Camera IP Camera IoT Sensors Video Files Advantech s IVA Inference Systems familiarize themselves with information from a variety of video sources and formats, and Intelligent Video Analysis (IVA) events based on training datasets, can be usefully applied by customers looking to develop their own AI applications. Advantech provides additional proprietary APIs to help manage event Information and accelerate development schedules and a digital inputoutput pathway helps administrators quickly respond to event triggers. Customers can connect to digital outputs directly to access control systems, alarms, or public information systems with little integration effort. Advantech IVA Inference Systems bring the power of AI into your next generation of intelligent applications.

4 Inference System Scenario SDK B A Cameras collect video data Video processing inference system Object recognition trained DL algorithm Advantech SDK analyses video data to AI models Intelligent information applied to your problem Applications Smart City Traffic management Parking management Law enforcement Public safety Deep learning technology empowers intelligent video analytics which encourages a new era of public safety and smart city solutions. Training servers from massive video datasets, deep learning is making computers analyze data like humans with perceptive inputs and predictable outcomes. By deploying trained models to edge systems, each device can act as an intelligent eye in the city. Intelligent Surveillance People counting and tracking Face detection and recognition License plate recognition Event detection Intelligent video surveillance analyzes video footage in realtime and detects abnormal events or activities in order to prevent crime and accidents. Deep learning enables identifying, inspecting, and recognizing people, vehicles, and events to achieve higher accuracy without human intervention. People and vehicle recognition and tracking are essential to manage the security in buildings, workplaces, and public spaces.

5 Key Solution Advantages Server IPC Box DVP Full Range Product Portfolio Supports deep learning architecture from backend storage, training servers and networking equipment, to front end edge systems and cameras. Deep Learning Library The deep learning library SDK includes training and inference tools and methods to help you deploy AI into your applications. RealTime Video Analysis Realtime video analysis based on trained models can apply inference predictions immediately. Edge Inference System Wide performance range of systems with rich IO are suitable for a variety of deep learning applications. Solution Offering Training Server Model Name Height (1U = 1.75") Processor Support AGS913 Dual Intel Xeon E52600 v3v4 AGS923 HPC7320 1U 2U 3U Supports EATXATX MicroATX motherboard with dual processors Form Factor Support Proprietary Micro ATX, ATX, EATX 3 x PCIe x16 doubledepthcard Expansion Slots + 1 x PCIe x 8 FHHL card SKY6400 4U Dual Intel Xeon Scalable Series 4 x PCIe x16 doubledeckcard+ 1x PCIe x 8 singledeck FHFL card Slim ODD Bay 1 (ODD should be purchased separately) 3.5" (hotswappable) 2 (3.5" 2.5") Drive Bay 3.5" (internal) 2.5" (hotswappable) (3.5" 2.5") 8 2.5" (internal) 2 Chassis Fan Cooling Air Filter Chassis Intrusion Alarm Front IO Interface Rear IO 7x40x56 + 2x40x28 high speed fan Yes 2 x USB x USB 3.0 4x80x38 + 1x80x20 + 1x80 x38 (optional) high speed fan 2 (8cm141.9CFM) + 1 (6cm27.72CFM) Yes 2 x USB x USB x USB x 120 x x 80 x 38 (option) Miscellaneous LED Indicators Power status, HDD activity, LAN status,location, error message System: Power, HDD, LAN1, LAN2 HDD Tray: HDD Power and Activity LED Power status, HDD activity, LAN1 & LAN2 Environment Rear Panel Operating Temperature Operating Humidity Vibration (5~500 Hz) Shock Location, error message 0~40 C (32~104 F) 40 C Two reserved DB9 ports 0.5 G 1G 0.5 G 10 G (with 11ms duration, half since wave) Physical Characteristics Dimensions (W x H x D) 430 x 44 x 770 mm (16.9" x 1.7" x 30.3") 430 x 88 x 770 mm (16.9" x 3.4" x 30.3") x x 480 mm (16.79" x 5.2" x 18.9") 435 x 177 x 673 mm (17.1" x 6.9" x 26.4")

6 Edge Inference System Transportation Public Safety and Security Fanless and industrialgrade application Modularized expandability for different applications MIC7500AI MIC7900AI Fanless server for high performance computing in harsh environments 4x LANs and idoor supported for communication with sensors Service Robots Healthcare Compact size for flexible deployment High CPU computing power for vision guidance and analytics AiMC3202AI AiMC3422AI Minitower form factor with elegant design for medical applications Space for nvidia GTX1050 DVP Video Capture Card Model Name DVP7011HE DVP7016HE Compression SW H.264 SW H.264 Channels 1 1 DVP7011MHE SW H Host Interface PCIe x1 (Gen2) Mini PCIe x1 (Gen 2) PCIe M.2 Video Input Interface SDIHDMIDVI VGAYPbPr Composite SVideo 1 x HDMIDVI YPbPrVGA SDI Max. Display Resolution 1920 x x x 6050 Max. Recording Resolution 1920 x x x 3025 Audio Audio Inputs 1 x SDI, 1 x HDMI, 2 x RCA 1 x HDMI 2 x RCA 1 x SDI Audio (LR) Physical Characteristic Dimensions ( L x H ) 107 x 101 mm (4.2" x 3.9") 51 x 30 mm (2" x 1.1") 60 x 22 mm (2.3" x 0.8")

7 NVIDIA GPU Cheat Sheet Architecture GPU Power (W) PEAK (TELOPS) FP64 FP32 FP16 PEAK INT8 TIOPs GDDR5 Memory (GB) Memory Bandwidth (GBs) Kepler K40M K M40 24GB Maxwell M40 12GB M P P P100 PCIe 12GB P100 PCIe 16GB Pascal P100 SXM2 16GB GTX W GTX1050Ti 75W DGX x 16 8 x 732 NVIDIA Cards Model Name GPU Manufacturing Process Base Clock Boost Clock SKYTESLAP4 Telsa P4 16nm 810 MHz 1063 MHz GFXNG1050TIF165D GeForce GTX 1050Ti (GP107) 14nm 1290 MHz 1392 MHz Graphics Processing Unit Form Factor Card Interface CUDA Cores Single slot ATX 2560 PCI Express 3.0 (x16) Dual slot ATX 768 Floating Point Preformance 5.5 TFLOPs 2.1 TFLOPs Open GL OpenGL 4.5 Video Decoder HEVC, H.264, VC1, MEPG2, MPEG4 part 2 decode Memory Clock 6000 Mhz 8 Gbps 3504 MHz 7 Gbps Memory DDR Type Memory Bus 256bits GDDR5 128bits Memory Size 8GB 4GB External Power No Board Spec. Power Consumption Operating Temperature 75W 0 C~50 C Dimensions PCIe Low Profile PCIe Full Height

8 Regional Service & Customization Centers China Kunshan Taiwan Taipei Netherlands Eindhoven Poland Warsaw USA Milpitas, CA Worldwide Offices Greater China China Beijing Shanghai Shenzhen Chengdu Hong Kong Taiwan Neihu Xindian Taichung Kaohsiung Middle East and Africa Israel Asia Japan Tokyo Osaka Nagoya Korea Seoul Singapore Singapore Malaysia Kuala Lumpur Penang Thailand Bangkok India Bangalore Pune Indonesia Jakarta Australia Melbourne Europe Germany Munich Düsseldorf France Paris Italy Milano Benelux & Nordics Breda UK Newcastle London Poland Warsaw Russia Moscow St. Petersburg Czech Republic Ústí nad Orlicí Ireland Oranmore Americas North America Cincinnati Milpitas Irvine Ottawa Brazil São Paulo Mexico Mexico City