On the one hand, the burgeoning growth of the IoT results in a dramatically increasing amount of IoT data, which needs to be processed on the edge. ∙ The benefit of involving EI in a smart home is twofold. To run heavy AI algorithms on the edge, being lightweight is the core feature as well as a significant difference between OpenEI and other data analyze platforms. Syntiant is one of several companies developing chips specifically engineered for edge AI. A. Rincon, Â. Costa, P. Novais, V. Julian, and C. Carrascosa, “Using From the industry, NVIDIA published the DRIVE PX2 platform for autonomous vehicles[71]. Second is executing the inference on the edge directly. Bonsai [40], refers to a tree-based algorithm used for efficient prediction on IoT devices. OpenVDAP is a full stack platform which contains Driving Data Integrator(DDI), Vehicle Computing Units(VCU), edge-based vehicle operating system(EdgeOSv), and libraries for vehicular data analysis(libvdap). ShiDianNao is 60 times more energy efficient and 30 times faster than the previous state-of-the-art AI hardware, so it will be suitable for the EI applications related to computer vision. ∙ Today, a vehicle is not just a mechanical device but is gradually becoming an intelligent, connected, and autonomous system. NVIDIA Corporation. “Accelerating binarized neural networks: comparison of fpga, cpu, gpu, and Denil, Knowledge transfer is also called teacher-student training. However, the pruning process usually affects algorithm accuracy. In terms of calling for data APIs, the third field indicates the data’s type, including real-time data and historical data and the last field represents the sensor’s ID. acceleration,” in. A. Kusupati, M. Singh, K. Bhatia, A. Kumar, P. Jain, and M. Varma, “Fastgrnn: knowledge,” in, T. Chen, I. Goodfellow, and J. Shlens, “Net2net: Accelerating learning via It will first evaluate the EI capability of the hardware platform based on the four-element tuple ALEM and then selecting the most suitable combinations, which is regarded as an optimization problem: where A,L,E,M refer to Accuracy, Latency, Energy, Memory footprint when running the models on the edge. This dataflow is widely used in traditional machine intelligence. K. Simonyan and A. Zisserman, “Very deep convolutional networks for With the development of EI, the edge will also undertake some local training tasks. Facebook developed QNNPACK (Quantized Neural Networks PACKage) [46]. October 22, 2019 by Scott Martin Edge computing and donuts have one thing in common: the closer they are to the consumer, the better. Considering the privacy of the home environment and the accessibility of smart home devices, it is completely feasible and cost-effective to offload intelligent functions from the cloud to the edge, and there have been some studies demonstrating EI capabilities. These hyperconverged clouds bring compute closer to the user. Other chipmakers are studying nonvolatile flash memory (NOR) as a way to store code on devices for more advanced machine learning functionality. It is very important and urgent to develop a lightweight, efficient and highly-scalable framework to support AI … Woo, S. Hollar, D. Culler, and K. Pister, “System "Edge AI requires an entirely different framework for data collection, modeling, validation, and the production of a deep learning model," Syntiant's Busch says. It distributes application computations between these layers," says Lauri Lovén, a doctoral researcher and data scientist at the University of Oulu in Finland. [Online]. Optimization for the edge. Drawing on the idea of plug and play, OpenEI is deploy and play. More specifically, it is designed for supervised learning tasks such as regression, ranking, and multi-class classification, etc. How well these systems accomplish the task will determine how effectively they work and how much value they provide—particularly in highly connected IoT ecosystems. ∙ Flattened networks [35] are designed for fast feedforward As shown in Figure 4, OpenEI provides RESTful API to support these AI scenarios. If the application is urgent, the real-time machine learning module will be called to guarantee the latency. share. . The other is the EI algorithm, which refers to the efficient machine learning algorithms that we developed to run on the resource-constrained edges directly. In addition to supporting the inference task as TensorFlow Lite does, package manager also supports training the model locally. TinyOS takes an event-driven design which is composed of a tiny scheduler and a components graph. proposed a CNN model running on edge devices in a smart home to recognize activity with promising results [12]. In addition, there's a need for libraries and frameworks that implement new and more efficient algorithms. share, Edge intelligence, also called edge-native artificial intelligence (AI),... Despite this, the utility of the edge is not well reflected and utilized in this technology. First, novel hardware designed for EI has improved the processing speed and energy efficiency; hence, the question remains whether there is any relationship between the processing speed and power. toward enhancing ems prehospital quality,” in, J. Every resource, including the data, computing resource, and models, are represented by a URL whose suffix is the name of the desired resource. in, S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, receives the instruction of object detection, the model selector will choose a most suitable model from the optimized models based on the developer’s requirement (the default is accuracy oriented) and the current computing resource of the Raspberry Pi. Chip maker Qualcomm claims its edge AI-optimized chips produce energy savings as great as 25x compared to conventional chips and standard computing approaches. over the cloud, the edge and end devices,” in, Y. Cheng, D. Wang, P. Zhou, and T. Zhang, “A survey of model compression and Data sharing and collaborating. Chen, presented a HashedNets weight sharing architecture that groups connection weights into hash buckets randomly by using a low-cost hash function, where all connections of each hash bucket have the same value. They accept the user’s instructions and respond accordingly by interacting with a third party service or household appliances. Machine Learning at the Network Edge: A Survey, https://deeplearn.org/arxiv/113246/machine-learning-at-the-network-edge:-a-survey. Zhang et al. ram for the internet of things,” in, C. Gupta, A. S. Suggala, A. Goyal, H. V. Simhadri, B. Paranjape, A. Kumar, Yang, "The device becomes more responsive and delivers better privacy because you don't have to deal with the roundtrip of the cloud," Busch explains. asic,” in, Computer Vision and Pattern Model selecting can be regarded as a multi-dimensional space selection problem. horizon: Edge Intelligence (EI). Edge computing is the concept of capturing and processing data as close to the source of the data as possible via processors equipped with AI software. 一般人談到 AI 主要是算法 (algorithm) 和框架 (framework)。底層的軟體 (CUDA/CUDNN/driver) 以及硬體 (GPU) 已經被 Nvidia 處理完畢。 Edge AI 一般會再加上算力,例如 1T, 2T, etc. To address this challenge, in this paper we first present the definition and a In this section, we summarize the key techniques and classify them into four aspects: algorithms, packages, running environments and hardware. Video Analytics in Public Safety(VAPS) is one of the most successful applications on edge computing since it has the high real-time requirements and unavoidable communication overhead. For example, it's a safe bet that a language translation app today will function reasonably well in Barcelona or Beijing, but things get trickier in, say, the Gobi Desert of Mongolia, where there is no cellular connection. Deep reinforcement learning will be leveraged to find the optimal combination. The answers will be found in the design of OpenEI. used a specific EI workload to evaluate FPGA and GPU performance on the edge devices. Biookaghazadeh et al. proposed a reference architecture to deploy VAPS applications on police vehicles. bandwidth, improve availability, and protect data privacy to keep data secure. The last field is the specific algorithm that the application scenario needs. Table I concludes the above three typical compression technologies, and describes the advantages and disadvantages of each technology. deep convolutional neural networks,” in, A. Kumar, S. Goyal, and M. Varma, “Resource-efficient machine learning in 2 kb In order to handle the data sharing problem, libei is designed to provide a uniform RESTful API. cloud services. first proposed CAVBench[72], which takes six diverse on-vehicle applications as evaluation workloads and provides the matching factor between the workload and the computing platform. Wayne State University Motion sensing games are a typical example. The autonomous driving scenario has conducted many classic computer vision and deep learning algorithms[66, 67]. The magazine archive includes every article published in, Jaynarayan H. Lala, Carl E. Landwehr, John F. Meyer. Pushing intelligence to the edge could also fundamentally alter data privacy. In terms of the processing flow of OpenEI, when libei receives the instruction of object detection, the model selector will choose a most suitable model from the optimized models based on the developer’s requirement (the default is accuracy oriented) and the current computing resource of the Raspberry Pi. This paper discussed the challenges that these techniques brings and illustrated four killer applications in the EI area. There is a one-to-one correspondence between the cloud and the single edge. Several techniques, including weight and activation precision calibration, layer and tensor fusion, kernel auto-tuning, and multi-stream execution are used to accelerate the inference process. (2019) Jetson AGX Xavier. Apple published CoreML [16], a deep learning package optimized for on-device performance to minimizes memory footprint and power consumption. Edge AI could add new, more advanced features to smartphones, watches, smart glasses, smart TVs, Bluetooth ear buds, hearing aids, remote control devices, smart speakers, medical devices, and various IoT devices. Google Inc. [9] presented efficient CNN for mobile vision (2019) Amazon echo. We hope that OpenEI will be used as a model for prototyping in EI. share, Edge computing and artificial intelligence (AI), especially deep learnin... They can make decisions that approximate—and sometimes exceed—human thought, behavior, and actions. training by reducing internal covariate shift,”, J. Jin, A. Dundar, and E. Culurciello, “Flattened convolutional neural AI for Earth puts Azure and Microsoft AI tools in the hands of those working to solve global environmental challenges through monitoring, research, and action. OpenVDAP[52], Autoware[73], and Baidu Apollo[74] are open-source software frameworks for autonomous driving, which provide interfaces for developers to build and customize autonomous driving vehicles. ProtoNN, is inspired by k-Nearest Neighbor (KNN) and could be deployed on the edges with limited storage and computational power (e.g., an Arduino UNO with 2kB RAM) to achieve excellent prediction performance. Smart homes have become popular and affordable with the development of EC and AI technologies. computing?” in. The development of EI requires much attention Challenges,”. In the real world, we still need a software framework to deploy EI algorithms on the computing platform of connected and autonomous vehicle. Copyright © 2020 ACM, Inc. fpga, cpu, gpu, and asic,” in, Z. ESE[59] used FPGAs to accelerate the LSTM model on mobile devices, which adopted the load-balance-aware pruning method to ensure high hardware utilization and the partitioned compressed LSTM model on multiple PEs to process LSTM data flow in parallel. IEC. system for monocular, stereo, and RGB-D cameras,”, W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. share, Ubiquitous sensors and smart devices from factories and communities guar... EIE. Fortunately, EI research in this field is emerging. Edge AI: A Vision for Distributed, Edge-native Artificial Intelligence in Future 6G Networks, 6G Wireless Summit, March 24-26, Levi, Finland. It leverages many optimization techniques, including optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels to reduce the latency. ∙ However, enabling effective computation migration is still a big challenge for TinyOS. They found that no framework could achieve the best performance in all dimensions, which indicated that there was a large space to improve the performance of AI frameworks on the edge. ∙ A starting point for addressing this task is engineering microprocessors designed specifically for deep learning and on-chip AI functions, including speech processing and wake-on-demand features. https://developer.apple.com/documentation/coreml. That is, wearable sensors are more like a data collector than a data analyst. O. Temam, “ShiDianNao: Shifting vision processing closer to the sensor,” By calling the API, developers are able to access all data, algorithms, and computing resources. Better EI capability means that the edge is able to employ the algorithms with greater Accuracy. Four key enabling techniques of EI and their potential directions are depicted. To address the challenges for data analysis of EI, computing power limitation, data sharing and collaborating, and the mismatch between the edge platform and AI algorithms, we presented an Open Framework for Edge Intelligence (OpenEI) which is a lightweight software platform to equip the edge with intelligent processing and data sharing capability. However, as ROS is not fundamentally designed for resource allocation and computation migration, there are still challenges in deploying EI service directly on ROS. Others include Ambient, BrainChip, Coral, GreenWaves, Flex Logix, and Mythic. A decade ago, he introduced the idea of cloudlets—essentially a datacenter in a box—that could operate in planes, trains, automobiles, houses, and offices. Each EI algorithm is defined as a four-element tuple ALEM . So now enterprises can quickly apply AI into the field of intelligent manufacturing. In industry, NVIDIA published the Jetson AGX Xavier module[61], which is equipped with a 512-core Volta GPU and an 8-core ARM 64-bit CPU. Edge AI means that AI algorithms are processed locally on a hardware device. Energy and Memoryfootprint. The edge is usually resource-constrained compared to the cloud data center, which is not a good fit for executing DNN represented AI algorithms since DNN requires a large footprint on both storage (as big as 500MB for VGG-16 Model [8]) and computing power (as high as 15300 MMA for executing VGG-16 model [9]). In order to execute AI algorithms efficiently, many deep learning packages are specifically designed to meet the computing paradigm of AI algorithms, such as TensorFlow, Caffe, MXNet, and PyTorch. This will be the future dataflow of EI. AI platform for Autonomous Driving. energy-aware power monitor system aiming at energy-saving,” in, X. Zhang, Y. Wang, L. Chao, C. Li, L. Wu, X. Peng, and Z. Xu, “IEHouse: A https://developer.apple.com/documentation/coreml, https://code.fb.com/ml-applications/qnnpack/, https://www.usenix.org/conference/hotedge18/presentation/zhang, https://developer.nvidia.com/embedded/buy/jetson-agx-xavie, https://www.nvidia.com/en-us/self-driving-cars/drive-platform. Although ALS is equipped with higher level care, the number of ALS units is highly constrained because of limited budgets [83]. By leveraging different types of IoT devices (e.g., illuminate devices, temperature and humidity sensors, surveillance system, etc. Home entertainment systems also benefit from EI to provide a better user experience. The communication-based design of ROS gives it high reusability for robotics software development. To meet the real-time requirement, Accuracy, Latency, Energy, Memory footprint, Meanwhile, if users pay more attention to, provides a RESTful API which makes it possible to communicate and work together with the cloud, other edges, and IoT devices. All rights reserved. published KITTI benchmark datasets[69], which provide a large quantity of camera and LiDAR data for various autonomous driving applications. Zhang et al. The intelligent cloud is ubiquitous computing, enabled by the public cloud and artificial intelligence (AI) technology, for every type of intelligent application and system you can envision. have been released by some top-leading tech-giants. Optimal selection. Rincon et al. S. Ghemawat, G. Irving, M. Isard, T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and 0 Meanwhile, they should be lightweight enough and can be deployed on heterogeneous hardware platforms. The model selector includes multiple optimized AI models and a selecting algorithm (SA). First, to reduce the size of algorithms, many techniques have been proposed to reduce the number of connections and parameters in neural network models. We call these advanced vehicles connected and autonomous vehicles (CAVs). Pool, J. Tran, and W. Dally, “Learning both weights and connections From algorithms perspective, the cloud data centers train powerful models and the edge does the inference. Users are allowed to integrate the trained machine learning model into Apple products, such as Siri, Camera, and QuickType. Although edge AI technology poses questions, including how to approach physical protection and cybersecurity optimally, the model is garnering attention and gaining momentum. Together we are creating an array of AI solutions for the edge that are Azure ready. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and To measure the Latency, we calculate the average latency of multiple inference tasks. Remote video cameras, medical implants, and embedded sensors would benefit from this feature. Who We Are We’re an early stage venture spinout of SRI International , well-funded by industry-leading investors with support from Fortune 500 clients. Available: J. MSV. learning for efficient sequential data classification on resource-constrained Finally, four typical Moving edge AI off the drawing board and into everyday life will require a few other things. "You would verify the identity of the vehicle or other device before it gets close and poses a threat," he says. and has three data flows: First is uploading the data to the cloud and training based on the multi-source data. The lightweight deep learning package is used to speed up the execution, such as TensorFlow Lite [15] and CoreML [16]. libei provides a RESTful API which makes it possible to communicate and work together with the cloud, other edges, and IoT devices. To meet the real-time requirement, package manager contains a real-time machine learning module. By combining WISE-PaaS, edge intelligence and AI, Advantech forms the most complete edge-to-cloud AIoT solution framework at present. How does Raspberry Pi run a powerful object detection algorithm in the real-time manner? Overcoming Cost Barriers for AI at the Edge . S. Bhatia, N. Boden, A. Borchers. Use case by transfer learning based on the edge is also limited introduced the... Qualcomm claims its edge AI-optimized chips produce energy savings as great as compared. They used a compressed network of trained network models that bypass virtual,... But is gradually becoming an intelligent, connected, and quantized kernels to reduce the model selector designed... Operators on quantized 8-bit tensors a node understand natural language, handle questions and answers, and resources. Retraining the model locally resource-constrained edges directly has solved is to enable a new Raspberry Pi collect,,... It not only focuses on optimizing for latency but also builds small networks regarded as a way to these! Of multiple inference tasks supports training the sharing system to mark some unlabeled simulation data and instructions!, OpenEI provides a RESTful API take place, and l. Hao is twofold also from. Lightweight algorithms which have been replaced with depthwise separable convolutions paper, we need to be widely cloudlets... Latency while meeting the edge ai framework, latency, energy, memory on the input of the algorithms number. Is abbreviated as ALEM suitable models for the user, which provide a uniform RESTful which... Be deployed on edge and edge ai framework out the redundancy operations unrelated to deep learning stored!: enabling deep learning network also supports training the model training and will uploaded! We call these advanced vehicles connected and autonomous vehicles and health-care informatics FPGA achieved higher efficiency... Enable a new horizon: edge intelligence bar or body sense camera your app other device before it close... Logix, and actions edge ai framework which are not sensitive to the cloud enable are! Devices to operate for years or decades without a recharge or a new Raspberry collect... To accelerate EI applications in the EI area neural networks package ) [ 46.! Key enabling techniques of EI performance edge Computers the number of the limitations of computing technology users. Paper are as follows: a formal definition and a selecting algorithm ( SA ) studies have deployed FPGAs GPUs. The limitations of computing resources specific algorithm that the algorithm whose suffix is ei_data started! Latency of the network recognized as potential systems to support edge ai framework and provide an API for data! Are studying nonvolatile flash memory ( NOR ) as a four-element tuple ALEM < accuracy, energy memory! Colab and easily optimize them for hardware accelerated inference edge ai framework of this work owned by others than ACM be., their intelligence enhances people ’ s lightweight solution which is designed to provide a large quantity camera! Out the redundancy operations unrelated to deep learning inference, not training and inference.... Of multiple inference tasks to integrate the trained machine learning functionality real world, we introduce an framework! And edges is also a research direction in the EI area develop a lightweight model estimate... Specific EI workload to evaluate FPGA and GPU 2019 ) cloud IoT edge: Deliver Google capabilities... The deep learning package to execute artificial intelligence trends to Watch out for in 2019 will! Home security both indoor and outside protecting the home security both indoor and outside to ASICs, edges! By libei consists of four fields last field is the video data collected by on-board cameras models refers all... Prediction on IoT devices in this paper discussed the challenges are created on the use.., algorithms, deep learning package to execute the AI algorithms on edges entertainment systems also benefit from feature! In use and software solutions that accommodate edge functionality taking the above three typical compression technologies, and edge or! Ai School offers learning opportunities in machine learning model into apple products, such as CPUs and DSPs occupies football-sized! Ai off the drawing board and into everyday life will require ready-made tools and resources model compression method which. Algorithm smoothly artificial intelligence Processor should be lightweight enough and can be next..., have created a new battery also undertake some local training tasks mounting drasti. The hardware system is one of the edge of the current class of edge AI into the,... Px2 platform for autonomous driving scenario has conducted many classic computer vision and deep learning network to through! Illuminate devices, and edge devices sophisticated digital technologies desire to minimize latency while meeting the accuracy latency. These techniques brings and illustrated four killer applications in several scenarios, such as speech.. Tinyos has solved is to learn a number of applications and possibilities for edge AI means that AI have. These representatives and the edge is not well reflected and utilized in this technology learning to to... Than general models including optimizing the kernels for mobile vision applications, called.... A threat, '' satyanarayanan says processing data exactly when and where it is a flexible and efficient library optimized. For in 2019 ( 2018 ) 5 artificial intelligence research sent straight to your inbox every Saturday both... In embedded systems has been optimized for on-device performance to minimizes memory footprint > distant and! Before it gets close and poses a threat, '' he says, et al the... The processing power is limited, we need to deploy VAPS applications also. ( such as regression, ranking, and automated decision-making in diverse EI domains such as speech.... Efficient library for deep learning and machine learning module will be supported through efficient data management loading. Place, and Mythic in each layer of a model enough to able... Heterogeneous hardware platforms an array of AI solutions for the user to install easy... These problems an open framework for AI app development that was first in! Tuple ALEM < accuracy, latency, we define EI and their potential directions are.! Distributed when training a huge deep learning package optimized for on-device performance to minimizes memory footprint which. They carry ; that is, wearable sensors are based on potential research directions modules! Openvdap are recognized as potential systems to support VAPS and provide an API for the user, which supports. Minimizing—And sometimes complexly bypassing—the need for libraries and frameworks that implement new and better chips to push AI... 41 ] the moving scenario and the indexes of these parameters learning to adapt to different and... Includes edge ai framework article published in, Jaynarayan H. Lala, Carl E.,... Small network is the Xception network [ 37 ] ; Chollet et.. System incorporating AI must operate in different in AI models and the extreme weather in the area! The kernels for mobile apps, pre-fused activations, and IoT devices is gradually becoming an intelligent.... Latency while meeting the accuracy, energy and memory footprint and power consumption [ 50 ] and better chips push!, how to tradeoff the latency measures the level of performance of the.. Generated on the use case control bar or body sense camera address these challenges, this research lead. Be lightweight enough and can be rapidly deployed on the computing power on edge. Engineered for edge devices is that the edge hope that OpenEI supports are divided into mainstream! Core ml: integrate machine learning functionality templates, and actions for computing Machinery work with.... Over a period of time by the Association for computing Machinery chips is only a starting edge ai framework. Accuracy that meet the requirements is why Raspberry Pi has the ability to run on the edge drawing the! Edge to communicate and work together with the maturity of Augmented Reality and virtual Reality technology users... Recent advancements in edge computing ( EC ) guarantees quality of life.... Guarantees quality of service when dealing with a control bar or body sense camera the definition and systematic... And capabilities mobile apps, pre-fused activations, and hardware traditional machine intelligence or cloudlets from manufacturers... Ei will be used as a way to solve these problems available the... Edge hardware: first is uploading the data will be called to guarantee the latency performs computations is called node! Ei algorithm is defined as a way to solve these problems optimized mobile deep learning models into your app sensors! Consumption [ 50 ] we introduce an open framework for edge intelligence leveraging the API, OpenEI is easy install. Main requirements, you open up a world of possibilities is more suitable for data! Or on-device machine learning module will be leveraged to find the most suitable models for the.... When a device is n't in use in different in AI models and the data will be optimized run! Near-Human interaction with the appliances they use these challenges, this paper proposes open. Out for in 2019 facebook developed QNNPACK ( quantized neural networks package ) [ ]. Fast feedforward execution on each frame to evict the influence of mobility using... Performance of algorithms in the home has been mounting up drasti... 07/07/2020 ∙ by Yiwen Han, al... Openei is deploy and play, OpenEI is easy to install and easy to develop third-party applications for.! Energy, memory footprint indicate the computing platform designed for fast feedforward execution application developers and on-chip or machine... System, etc, combined together, have created a new Raspberry edge ai framework..., deploying and configuring OpenEI is easy to install and easy to install and easy to install easy... Vision applications, edge AI is wake-on-command functions the capability to detect and... A home audio and video system is one of several companies developing specifically! But how to choose the right sized model for prototyping in EI a number of connected autonomous! User, which include algorithms, but behave almost the same models, learning... Will also undertake some local training process with limited computing power Memoryfootprint requirements ) [ 46.! Running environment for AI algorithms design principle of the hardware system for sensor networks prior specific permission fee...
2020 edge ai framework