cloud services. Forbes listed the convergence of IoT and AI on the edge as one of five AI trends in 2019 [4]. [20] proposed a binary neural network to quantify the weights. MUVR is proposed in this scenario to boost the multi-user gaming experience with the edge caching mechanism [80]. Edge AI application developers and on-chip or on-device machine learning tasks will require ready-made tools and resources. non-intrusive household appliance state recognition system,” in, R. Abdallah, L. Xu, and W. Shi, “Lessons and experiences of a DIY smart Remote video cameras, medical implants, and embedded sensors would benefit from this feature. ∙ Four key enabling techniques of EI and their potential directions are depicted. Learn about AI on Azure. aXeleRate – Keras-Based Framework for AI on the Edge. Edge AI takes direct aim at these issues. Sometimes, edges will retrain the model by transfer learning based on the data they generated. 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. Another small network is the Xception network [37]; Chollet et al. Computing power limitation. 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. share, In recent years the two trends of edge computing and artificial intellig... ", Satyanarayanan, M. and Davies, N. Energy and Memoryfootprint. How does Raspberry Pi run a powerful object detection algorithm in the real-time manner? To support EI, many techniques have been developed, called EI techniques, which include algorithms, software, and hardware. Parameter sharing and pruning method control the capacity and storage cost by reducing the number of parameters which are not sensitive to the performance. share, Ubiquitous sensors and smart devices from factories and communities guar... “Low-rank matrix factorization for deep neural network training with Syntiant is one of several companies developing chips specifically engineered for edge AI. Model selecting can be regarded as a multi-dimensional space selection problem. Although ALS is equipped with higher level care, the number of ALS units is highly constrained because of limited budgets [83]. Available: B. L. Mearian. This section will illustrate the typical application scenarios and discuss how to leverage OpenEI to support these applications. To address these challenges, this paper proposes an Open Framework for Edge Intelligence. Request permission to publish from [email protected] or fax (212) 869-0481. execution. In the EI scenario, advanced AI models based on machine learning algorithms will be optimized to run on the edge. . In this paper, we define EI as a capability that enables edges to execute artificial intelligence algorithms. As humans, things, and AI … As an important EI scenario, OpenEI provides APIs to call the AI algorithms related to the smart home. Open Problems: There is a crucial open problem that needs to be addressed: how to design a lightweight edge operating system with high availability. EdgeX is a Linux Foundation project that is focused on providing a framework for IoT edge computing. As shown in Figure 4, OpenEI provides RESTful API to support these AI scenarios. networks for feedforward acceleration,”, M. Wang, B. Liu, and H. Foroosh, “Factorized convolutional neural networks.” Meanwhile, the edge operating system should be light enough to be implemented on the computing resource-constraint edge. When the module is called, the machine learning task will be set to the highest priority to ensure that it has as many computing resources as possible. Edge AI: enabling Deep Learning and Machine Learning with High Performance Edge Computers The number of connected devices collecting data is continually expanding. Intelligence in the home has been developed to some extent, and related products are available on the market. in, F. Chollet, “Xception: Deep learning with depthwise separable convolutions,”, F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, Developers of AI applications for edge deployment are doing their work in a growing range of frameworks and deploying their models to myriad hardware, software, and cloud environments. From the software perspective, the cloud runs the cluster operating system and deep learning framework, such as TensorFlow [13] and MXNet [14]. 11/25/2020 ∙ by Di Liu, et al. The first aspect is from the algorithm perspective, which is aimed at designing a lightweight model to support EI. changes,”, J. Andreu-Perez, C. C. Poon, R. D. Merrifield, S. T. Wong, and G.-Z. "The same Xeon hardware that occupies a football-sized building would be adapted to a small box or rack to fit the environment. 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. For each service, the program or features are divided into several small pieces and distributed on several nodes, and the ROS topic is defined to share messages between ROS nodes. However, one drawback is that edge devices are not powerful enough to implement large neural networks; the other is that the vibration in a video frame makes it more difficult to analyze. As one of the most intelligent devices in the smart home ecosystem, smart speaker such as Amazon Echo [75], Google Home [76] are quite promising models that involve in EI. 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. And thus, we design the “In-Edge AI” framework in order to intelligent-ly utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the mod- resource constrained edge cloud,” in, F. Martin-Sanchez and K. Verspoor, “Big data in medicine is driving big One of the main tasks of packages is to learn a number of weights in each layer of a model. At the heart of edge AI is a simple but profound challenge: getting computing systems to make decisions at the pace of the human mind and real-time events. We envision that there missing a After that, the Raspberry Pi is able to detect multiple objects directly based on the data collected by the camera on board and meet the real-time requirement. With EI involved, the system handles the user’s personalized recommendation service by itself, without uploading any privacy data about the user’s preferences to the cloud, so that the user has a smoother and safer entertainment experience. Pushing decision-making and other functions to the edge of the network produces dramatic changes, Verma says. Every resource, including the data, computing resource, and models, are represented by a URL whose suffix is the name of the desired resource. In this section, we introduce an Open Framework for Edge Intelligence (OpenEI), a lightweight software platform to equip the edge with intelligent processing and data sharing capability. It distributes application computations between these layers," says Lauri Lovén, a doctoral researcher and data scientist at the University of Oulu in Finland. The two hyper-parameters that Google introduced allow the model builder to choose the right sized model for the specific application. This method only needs to save the values of these representatives and the indexes of these parameters. Meanwhile, if users pay more attention to Accuracy, the optimization target will be replaced by maximize A and the constraints are L, E, and M. The same is true of other requirements, i.e. [Online]. Yet, the current class of edge AI chips is only a starting point. Copyright © 2020 by the ACM. (2019) NVIDIA DRIVE PX2: Scalable Optimization for the edge. These two trends, combined together, have created a new Several questions may arise: how does Raspberry Pi collect, save, and share data? A fundamental shift in AI Training A Distributed yet Collaborative Framework for training DL and ML at the Edge We allow any company, from any industry, to train complete DL and ML models, directly on their own edge devices. [21], employed the k-means clustering algorithm to quantize the weights of fully connected layers, which could achieve up to 24 times the compression of the network with only 1% loss of classification accuracy for the CNN network in the ImageNet challenge. The Dark Triad and Insider Threats in Cyber Security, https://ieeexplore.ieee.org/document/8747287, http://jultika.oulu.fi/files/nbnfi-fe2019050314180.pdf, https://ieeexplore.ieee.org/abstract/document/8789967, https://deeplearn.org/arxiv/113246/machine-learning-at-the-network-edge:-a-survey, National Guard Called in to Thwart Cyberattack in Louisiana Weeks Before Election, Autonomous Vehicle Safety: Lessons from Aviation, Here's Why Resentment is the Key to Happiness, Interconnecting Cisco Networking Devices Part 1 (ICND1) v1.0. 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. m refers to the selected models and Models refers to all the models. Emerging memory technologies like Magnetoresistive Random-access Memory (MRAM) and Resistive Random-Access memory (ReRAM) could further optimize performance and power for specific uses cases, including ultra-low-power applications running independent of a data center. [Online]. ∙ architecture directions for networked sensors,”, M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and The objective is to enable the edge server to mask the private information before uploading the data. on the edges and its application to amber alerts,”, L. Liu, X. Zhang, M. Qiao, and W. Shi, “SafeShareRide: Edge-based attack Epro and Mpro are the energy and memory footprint that the edge provides. ∙ Most of the current technologies for smart wearable sensors are based on cloud computing because of the limitations of computing resources and capabilities. Available: G. Plastiras, M. Terzi, C. Kyrkou, and T. Theocharidcs, “Edge intelligence: At some point, this research could lead to vehicles and other machines that can be detected by a unique acoustical signature. By leveraging different types of IoT devices (e.g., illuminate devices, temperature and humidity sensors, surveillance system, etc. 07/07/2020 ∙ by Sean Wang, et al. Users are allowed to integrate the trained machine learning model into Apple products, such as Siri, Camera, and QuickType. Meanwhile, with the maturity of Augmented Reality and Virtual Reality technology, users are able to have a better game immersive experience. Cisco Global Cloud Index [2]estimates that there will be 10 times more useful data being created (85 ZB) than being stored or used (7.2 ZB) by 2021, and EC is a potential technology to help bridge this gap. 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. In this paper, we define IoT as the billions of physical devices around the world that are securely connected to the Internet, individually or collaboratively, collecting and sharing data, applying intelligence to actuate the physical world in a safe way. 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. We also hope that this paper provides helpful information to researchers and practitioners from various disciplines when designing new developed IEHouse, a non-intrusive status recognition system, for household appliance [78] with the assistance of deep neural networks. from industry and academia due to its promise to reduce latency, save Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, caching and communication. Inside OpenVDAP, VCU supports EI by allocating hardware resources according to an application, and libvdap supports EI by providing multi-versions of models to accelerate the model inference. “Big data for health,”, X. Wu, R. Dunne, Z. Yu, and W. Shi, “STREMS: a smart real-time solution TensorRT is a platform for high-performance deep learning inference, not training and will be deployed on the cloud and edge platforms. 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. We call these advanced vehicles connected and autonomous vehicles (CAVs). [Online]. Energy refers to the increased power consumption of the hardware when executing the inference task. in, S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, The development of EI comes from two aspects. https://azure.microsoft.com/en-us/services/iot-edge/, https://basecamp.iec.ch/download/iec-white-paper-edge-intelligence-en/. networks with the hashing trick,” in, P. J. Werbos, “Backpropagation through time: what it does and how to do it,”, S. Han, J. Health and biomedicine are entering a data-driven epoch, First is pre-hospital emergency medicine, where the emergent patient is been cared for before reaching the hospital, or during emergency transfer between hospitals, emergency medical service (EMS) systems are provided in the form of basic life support (BLS) and advanced life support (ALS). Ph.D. dissertation, Citeseer, 2014. The first field is the IP address and port number of the edge. TensorFlow Lite [15] is TensorFlow’s lightweight solution which is designed for mobile and edge devices. 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. ), it is feasible to keep track of the internal state of a home and ensure its safety, comfort, and convenience under the guarantee of EI. In ROS, the process that performs computations is called a node. Last, with the support of OpenEI, the edge will need to handle multiple tasks which raises the problem of how to execute multiple tasks on a package in the meantime. , there are at least three dimensions to choose, e.g., AI models, machine learning packages, and edge hardware. L. Sifre and S. Mallat, “Rigid-motion scattering for image classification,” This means a device can operate without a persistent connection to a dedicated network, or the Internet, and it can access remote connections and transfer data on an "as needed" basis. Every resource, including the data, computing resource, and models, are represented by a URL whose suffix is the name of the desired resource. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page. [Online]. SafeShareRide[64] is an edge based detection platform which enables a smartphone to conduct real-time detection including video analysis for both passengers and drivers in ridesharing services. ∙ 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. 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. Robot Operating System(ROS)[51] is recognized as the typical representative of next the generation of mobile operating systems to cope with the Internet of Things. This will be the future dataflow of EI. Microsoft provides Azure IoT Edge [5], a fully managed service, to deliver cloud intelligence locally by deploying and running AI algorithms and services on cross-platform edge devices. Subsequently. If the application is urgent, the real-time machine learning module will be called to guarantee the latency. The retrained models will be uploaded to the cloud and combined into a general and global model. A remarkable thing about artificial intelligence (AI) is how rapidly and dramatically it has crept into the mainstream of society. 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. The remainder of this paper is organized into six sections. Over the last couple of years, the idea of cloudlets and edge AI has begun to take shape. (2019) Hitech (health information technology for economic and clinical health) Subsequently, the URI http://ip:port/ei_algorithms/safety/detection/video=video will be visited to call for the object detection function and response the detection results to the developer. Second is smart wearable sensors. There is a need for new devices and network models that bypass virtual assistants, smart speakers, and the cloud. “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb A low-latency framework requires new chips, storage devices, and algorithms, however. The idea of knowledge transfer is to adopt a teacher-student strategy and use a pre-trained network to train a compact network for the same task[28]. Train your machine learning models in Google Colab and easily optimize them for hardware accelerated inference! AI applications, including connected health, connected vehicles, smart manufacturing, smart home, and video analytics call for running on the edge. With the development of AI and EC, EI emerges since it has the potential to reduced bandwidth and cost while maintaining the quality of service compared to processing on the cloud. Drawing on the idea of plug and play, OpenEI is deploy and play. The benefit of involving EI in a smart home is twofold. If the application is urgent, the real-time machine learning module will be called to guarantee the latency. "Existing frameworks such as Spark, Tensorflow, or Ray are essentially cloud-native, and their computational models are a poor fit to the edge environment," Lovén says. A. Kusupati, M. Singh, K. Bhatia, A. Kumar, P. Jain, and M. Varma, “Fastgrnn: Third, how to achieve collaborative learning on the cloud and edges is also a research direction. Framework for Edge Intelligence, OpenEI, which is a lightweight software platform to equip the edge with intel- ligent processing and data sharing capability.

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