字幕列表 影片播放 列印英文字幕 Hello everyone My name is Malcolm Today I'm very happy to be here to introduce IEI's new AI inference acceleration card Mustang-V100-MX8 V stands for VPU MX8 means there are 8 Intel® Movidius™ Myriad™ MA2485 inside this PCIe card With Intel OpenVINO™ toolkit You can surf on deep learning inference acceleration by Mustang-V100-MX8 Before we introduce the new product Let's talk about why IEI designs this new product As we know IEI is a leading company of hardware design and QNAP is one of IEI group companies that has strong software capability Due to the AI chip capability is getting stronger and AI algorithm is getting higher accuracy than ever we expect the AI demand will increase dramatically in the future That's why IEI cooperates with Intel to design AI acceleration cards and in the meanwhile QNAP invests lots of resources to focus on AI industry From factory inspection smart retail to medical assistance systems there are more and more AI applications in our everyday life Actually, AI is not going to replace human at all we use AI to assist human because it's never get fatigue and will not affect it's judgment by other undesired factors Let's have some recap of AI process In traditional machine learning engineer and data scientist have to define the feature of input image. For example in this case we have to create the feature of ears shape mouse shapes and tails appearance manually then it can predict the input image Deep learning progress We have to prepare the tagged training image Use the suitable topology to train the model The features will be created automatically by the algorithm In this case deep learning starts to extract edge in the first layer get bigger region such as nose ear leg in deeper layer and finally predict the input image which means 90% dog in this case The main differences between traditional machine learning and deep learning are machine learning have to highlight the feature manually by engineer and data scientist Deep learning will generate by topology itself the second difference is the more correct data will help deep learning to get higher accuracy That's why in recent year deep learning methods can almost reach human judgment in the ImageNET contest This page includes deep learning tool frameworks Above OS there is framework for deep learning such as TensorFlow Caffe and MXNet Then use the suitable topology to train deep learning model Let's say for Image classification task you may select AlexNet GoogleNet For object detection task you may choose SSD and YOLO Deep learning has two sections one is training another is inference The tag images generate trained model by a desired topology Then output the trained model into inference machine to predict the result The technology of training is almost well-established in today's industry but how to complete the inference process is the most important task today What is OpenVINO toolkit OpenVINO is short of Open Visual Inference & Neural Network Optimization It's an Intel open source SDK which can convert popular frameworks into Intel acceleration hardware The heterogeneous feature allows it to work in different acceleration platform such as CPU, GPU, FPGA, VPU It also includes model optimizer to convert pre-trained model from different frameworks to Intel format. Inference engine is a C++ API by which you can call API for inference applications Besides it also has optimized OpenCV and Intel® Media SDK to do the code works. Here is the OpenVINO™ toolkit workflow In the first step it converts the frameworks by model optimizer Then start by the left top corner video stream Because all video stream was encoded it needs to be decoded by Intel media SDK Then it needs some image preprocess to remove background or emphasize the image by morphology methods in OpenCV then call inference engine to predict the input image After the prediction it will need image post process which means use OpenCV to highlight the object you detect or add some text on the detect result then the final step is to encode the video then send it to other server. OpenVINO™ toolkit includes many examples and pre-trained models such as object recognition image classification age-gender recognition vehicle recognition Which you can download and get familiar with the OpenVINO toolkit interface from Intel official website In hospital there are many data that can be analyzed by AI methods Such as OCT and MRU images and other physical data from patients Let's take a look by one example. Macular degeneration would happen in senior citizens Once you found you have vision distortion it might be too late for medical treatment In this case, it uses ResNET to train 22000 tag OCT images by medical experts And it can predict wet dry, normal macular condition by AI method. We can see from the left hand side picture it is the deep learning application without OpenVINO toolkit the frame rate is about 1.5 fps. In the right hand side picture, using OpenVINO toolkit the performance is 28 fps which is almost 20 times increasing in this application. Here is the IEI Mustang acceleration card series Including CPU FPGA and VPU, VPU VPU means Mustang-V100-MX8 They are all based on OpenVINO toolkit to implement on deep learning inference applications The features of Mustang-V100-XM8 It's a very compact PCIe card which has half-length single slot dimension The power consumption is 30 W which is extremely low 8 Intel® Movidius™ Myriad™ X VPU inside provide powerful computation capability It's ideal for edge computation. Other features are wide temperature rang for operating in 0~55 degree Celsius and supporting multiple cards It can also support popular topologies such as AlexNet, GoogleNet, SSD and YOLO another great feature Mustang-V100-MX8 is a decentralized computing platform It can distribute difference VPU for different video input and even different topology for each VPU Which has very high flexibility for your applications Mustang-V100-MX8 supports topology such as AlexNet, GoogleNet It's ideal for image classification SSD, YOLO is suitable for object detection and applications Here is a comparison table of FPGA and VPU acceleration cards which can help user to choose what's the ideal card for their applications VPU is an ASIC which has less flexibility compared to FPGA But with it's extremely low power consumption and high performance it's very suitable for edge device inference systems Let's introduce IEI's AI acceleration cards roadmap CPU and FPGA acceleration cards are already launched and VPU acceleration card will launch in December more and more SKU such as mini PCIe and M.2 acceleration cards interface will be ready soon. Here we introduce an ideal IEI system for AI deep learning FLEX-BX200 is a 2U compact chassis with rich I/O and can connect to FLEX PPC to become a IP 66 high level water and dust proof system which is ideal for the environment of traditional industry TANK AIOT development kit is an Intel proved OPENVINO toolkit platform with pre-installed OpenVINO toolkit It's an OpenVINO ready kit it can develop your deep learning inference applications with IEI VPU FPGA acceleration cards right away Here is an example In this demo we are using TANK-AIOT Development Kit and the Mustang-V100-MX8 with OpenVINO toolkit to run an Intel pre-trained model of vehicle classification Let's start the demonstration Here we have TANK-AIOT Development Kit combining with our Mustang-V100-MX8 to process the OpenVINO pre-trained model about the vehicle recognition In this demonstration it's using GoogleNET and YOLO to do the car detection and vehicle model recognition so you can see from the laptop corner the frame rate is around 190 and 200 which means its car computation capability is extremely high Mustang-V100-MX8 has the features about very low consumption and also very compact size which is an ideal acceleration card for your edge computation device That's today's demonstration Mustang-V100-MX8 is an ideal acceleration card for the AI deep learning applications Hope you can understand more by today's introduction and the demonstrations If you have more question please contact us or scan the QR code to get more detail Thank you. Bye.