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deep learning on edge devices

∙ 0 ∙ share . Microsoft In this book chapter, we aim to provide our insights for answering the following question: can edge computing leverage the amazing capability of deep learning? leverage the amazing capability of deep learning. The realization of this vision requires considerable innovation at the intersection of computer systems, networking, and machine learning. In such scenario, instead of running the DNN models locally, it is necessary to offload the execution of DNN models. Solving those challenges will enable resource-limited edge devices to ∙ Given the increasing heterogeneity in onboard computing units, mapping deep learning tasks and DNN models to the diverse set of onboard computing units is challenging. 03/14/2018 ∙ by Cihat Baktir, et al. 0 On the left, it is the end devices that train models from local data, with weights being aggregates at an edge device one level up. Combine latency with the time it takes to compute a recommended selection of movies for the millions of users, and you’ve got a pretty subpar service. Practical Deep Learning … To address this challenge, the opportunities lie at exploiting the redundancy of DNN models in terms of parameter representation and network architecture. Although the Internet is the backbone How to best protect users’ privacy while still obtaining well-trained DNN models becomes a challenging problem. However, some sensors that edge devices heavily count on to collect data from individuals and the physical world such as cameras are designed to capture high-quality data, which are power hungry. To overcome this issue, [li2016pruning] proposed a model compression technique that prunes out unimportant filters which effectively reduces the computational cost of DNN models. For the remainder of this blog, we’ll dive a bit deeper into Edge Computing paradigms to get a better understanding of how it can improve our deep learning systems, from training to inference. It must retrain itself. Netflix has a powerful recommendation system to suggest movies for you to watch. 22, no. With such personal information, on-device training is enabling training personalized DNN models that deliver personalized services to maximally enhance user experiences. : Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. More problems in machine learning are solved with the advanced techniques that researchers discover by the day. You will learn how to use Python for IoT Edge Device applications, including the use of Python to access input & output (IO) devices, edge device to cloud-connectivity, local storage of edge parameters and hosting of a machine learning model. Sounds like a job for the cloud, right? These devices, referred to as edge devices, are physical devices equipped with sensing, computing, and communication capabilities. Can it still perform after that? For a DNN model, the amount of information generated out of each layer decreases from lower layers to higher layers. Congress Wants to Protect You From Biased Algorithms, Deepfakes, and Other Bad AI. The era of edge computing has arrived. Federated Learning: Federated Learning (FL) is also an emerging deep learning mechanism for training among end, edge, and cloud. 1–7, doi: 10.1109/PerComWorkshops48775.2020.9156225. However, the ability to deploy and scale deep learning on edge devices, with a light footprint and efficient memory and processing power, is … Unfortunately, cloud computing suffers from three key drawbacks that make it less favorable to applications and services enabled by edge devices. Abstract:The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. I will also briefly introduce a paper that discusses an edge computing application for smart traffic intersection and use it as context to make the following concepts make more sense. One of the first companies out of the gate is Hailo with its Hailo-8 DL processor for edge devices. ML-enabled services such as recommendation engines, image and speech recognition, and natural language processing on the edge … ∙ for IoT and Mobile Edge Computing Applications, Cloud No Longer a Silver Bullet, Edge to the Rescue. 10/17/2020 ∙ by Mi Zhang, et al. Coordination between training and inference — Consider a deployed traffic monitoring system has to adjust for after road construction, weather/changing seasons. To accomplish this task, our DNN must be capable of detection of humans as well as recognition, to make sure we find the right child (their parent shares a photo so we know what to look for). However, we can still host a smaller DNN that can get results back to the end devices quickly. It is the one that is going to bring the most disruption and the most opportunity over the next decade. inspire new research that will eventually lead to the realization of the vision This is edge intelligence. Therefore, this distributed system successfully completes the same task that normally would be allocated to the cloud. 08/03/2020 ∙ by Ahnaf Hannan Lodhi, et al. Partitioning at lower layers would prevent more information from being transmitted, thus preserving more privacy. Moreover, edge devices are much cheaper if they’re fabricated in bulk, reducing the cost significantly. For example, [han2015deep] proposed a model compression technique that prunes out unimportant model parameters whose values are lower than a threshold. However, existing works in deep learning show that DNN models exhibit layer-wise semantics where bottom layers extract basic structures and low-level features while layers at upper levels extract complex structures and high-level features. This architecture is divided into three levels: end, edge, and cloud. We’ll share lessons learned from three real-world applications in which Hailo’s deep learning processor is being used to implement deep learning. This is because these DNN models are designed for achieving high accuracy without taking resources consumption into consideration. share, The increasing use of Internet-of-Things (IoT) devices for monitoring a ... ), we might also be interested in practical training principles at edge. On the right, training data is instead fed to edge nodes that progressively aggregate weights up the hierarchy. If you’re interested in learning more about any topic covered here, there are plenty of examples, figures, and references in the full 35-page survey: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8976180. ∙ Federated learning can address several key challenges in edge computing networks: Non-IID training data, limited communication, unbalanced contribution, and privacy and security. When Will A.I. You might ask why this is important at all, but it turns out that as our products and services become more complex and sophisticated, new problems arise from latency, privacy, scalability, energy cost, or reliability perspectives. It also hosts an extraordinary amount of content on its servers that it needs to distribute. The second mode is DNN processing mode that is optimized for deep learning tasks. To do this, that means the cloud is not a delegator of data. Besides data heterogeneity, edge devices are also confronted with heterogeneity in on-device computing units. The ability to deploy a system like this dramatically increases the potential for system deployment in places further away — or completely disconnected — from the cloud! Directly uploading those raw data onto the cloud constitutes a great danger to individuals’ privacy. To answer this question, the first aspect that needs to take into account is the size of intermediate results of executing a DNN model. 2, pp. Second, data collected at edge devices may contain very sensitive and private information about individuals. It seems that we might have an interest in machine learning models that can be adapted to changing conditions. Posted by Navendu Pottekkat on May 24, 2020 As the … 0 There are early works that explored the feasibility of removing ADC and directly using analog sensor signals as inputs for DNN models [likamwa2016redeye]. To realize the full promise of deep learning in the era of edge computing, there are daunting challenges to address. Constructions projects can happen any time, changing what an intersection looks like completely — in this case we may need to retrain our models, possibly while still performing inference. As a result, the trained DNN models become more robust to the various noisy factors in the real world. This figure shows two examples of a distributed training network. While all of training runs exclusively in the datacenter, there is an increasing push to transition in-ference execution, especially deep learning, to the edge. At the edge level, we have both a minority of the network shared with the cloud alongside a smaller, trained deep neural network. In terms of input data sharing, currently, data acquisition for concurrently running deep learning tasks on edge devices is exclusive. As such, a deep learning task is able to acquire data without interfering other tasks. Data augmentation techniques generate variations that mimic the variations occurred in the real-world settings. Second, while sensor data such as raw images are high-resolution, DNN models are designed to process images at a much lower resolution (e.g., 224×224). How to effectively share the data inputs across concurrent deep learning tasks and efficiently utilize the shared resources to maximize the overall performance of all the concurrent deep learning tasks is challenging. First, we’ll examine a video analytics use case, where multiple video streams are processed at the edge … For example, [radu2016towards]. Similarly. When it comes to AI based applications, there is a need to counter latency constraints and strategize to speed up the inference. We want to make crossing the street safer for pedestrians while more cars become self-driven. To address this input data sharing challenge, one opportunity lies at creating a data provider that is transparent to deep learning tasks and sits between them and the operating system as shown in Figure 2. However, there are streaming applications that require sensors to be always on. Generalization of EEoI (Early Exit of Inference) — We dont always want to be responsible for choosing when to exit early. In terms of network architecture redundancy, state-of-the-art DNN models use overparameterized network architectures and thus many of their parameters are redundant. The mismatch between high-resolution raw images and low-resolution DNN models incurs considerable unnecessary energy consumption, including energy consumed to capture high-resolution raw images and energy consumed to convert high-resolution raw images to low-resolution ones to fit the DNN models. ∙ Much like edge intelligence, the intelligent edge brings content delivery and machine learning closer to the user. In this book chapter, we presented eight challenges at the intersection of computer systems, networking, and machine learning. Edge computing is revolutionizing the way we live, work, and interact with the world. State-of-the-art DNN models incorporate a diverse set of operations but can be generally grouped into two categories: parallel operations and sequential operations. Under this model, the resolutions of collected images are enforced to match the input requirement of DNN models. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. Here I will introduce the topic of edge computing, with context in deep learning applications. With more than 20 billion microcontrollers shipped a year, these chips are everywhere. Such discrepancy can be caused by variation in sensor hardware of edge devices as well as various noisy factors in the real world that degrade the quality of the test data. We can do this by adding computer vision systems to intersections to watch for potential collisions. Deep Learning models are known for … The sizes of intermediate results generated out of each layer have a pyramid shape (Figure 3), decreasing from lower layers to higher layers. learning-based approaches require a large volume of high-quality data to train All varieties of ma-chine learning models are being used in the datacen-ter, from RNNs to decision trees and logistic regres-sion [1]. So far we’ve talked about how we can stretch a DNN architecture across cloud, edge, and end devices. Finally, at the end level are our end devices. In terms of resource sharing, in common practice, DNN models are designed for individual deep learning tasks. We hope our discussion could inspire new research that turns the envisioned intelligent edge into reality. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. However, with an explosive field like deep learning finding new methods and applications, a entirely new field is being fueled to match and possibly surpass this demand. This leaves significant room for open-endedness — where we can apply DNNs or DRL for resource management such as caching (i.e. If we wanted to add a vision system to some of them, a centralized compute system is more than likely to come across bottlenecks for data processing. Read on to see how edge computing can help address these concerns! There may be synchronization issues because of edge device constraints (i.e. We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices … Every intersections is going to look a bit different from another, could you really train one vision system to work seamlessly at each intersection? and are very expensive in terms of computation, memory, and power consumption. ∙ For example, the convolution operations involved in convolutional neural networks (CNNs) are matrix multiplications that can be efficiently executed in parallel on GPUs which have the optimized architecture for executing parallel operations. Some examples are: Lastly (and before the details get too confusing! What if, instead, we used an edge platform specifically for finding the Region-of-Interest (RoI). for Dynamic Network Scheduling, Addressing the Challenges in Federating Edge Resources, State-of-the-art Techniques in Deep Edge Intelligence, pAElla: Edge-AI based Real-Time Malware Detection in Data Centers, Deep Learning-Based Multiple Object Visual Tracking on Embedded System A variety of concerns may rise regarding training. With the rising potential of edge computing and deep learning, the question is also raised as to how should we go about measuring performance of these new systems or determining compatibility across the end, edge, and cloud: On top of this, the introduction of edge hardware comes with its own unique challenges. Use of edge intelligence is one way we can address these concerns. We’ll begin with the two major paradigms within Edge Computing: edge intelligence and the intelligent edge. This paper takes the position that, while cognitive computing today reli... can edge computing leverage the amazing capability of deep learning? To address this challenge, we envision that the opportunities lie at exploring smart data subsampling techniques, matching data resolution to DNN models, and redesigning sensor hardware to make it low-power. The data provider creates a single copy of the sensor data inputs such that deep learning tasks that need to acquire data all access to this single copy for data acquisition. Edge intelligence brings a lot of the compute workload closer to the user, keeping information more secure, delivering content faster, and lessening the workload on centralized servers. Data obtained by these sensors are by nature heterogeneous and are diverse in format, dimensions, sampling rates, and scales. These tasks all share the same data inputs and the limited resources on the edge device. Deep learning models are known to be expensive in terms of computation, memory, and power consumption [he2016deep, simonyan2014very]. Hybrid model modification — our cloud model may need to be pruned to run on end nodes or end devices. Specifically, to ensure the robustness of DNN models in real-world settings, a large volume of training data that contain significant variations is needed. To address this challenge, we envision that the opportunities lie at exploring data augmentation techniques as well as designing noise-robust loss functions. For example, for real time training applications, aggregating data in time for training batches may incur high network costs. While a number of … Deep Learning on a Cluster of 100 Edge Devices En route to replacing the cloud for all AI training. 02/15/2018 ∙ by Yuhao Zhu, et al. This devices often use microcontrollers. Smart cities are perhaps one of the best examples to demonstrate the need and potential for edge compute systems. Large metropolitan cities easily have hundreds, if not thousands of intersections. The idea of edge intelligence is scalable too, we can imagine this on a country-wide scale or on the scale as simple as a single warehouse. One effective technique to overcome this dilemma is data augmentation. Danon shares lessons learned from three real-world applications in which Hailo’s deep learning processor… “Lessons Learned from the Deployment of Deep Learning Applications In Edge Devices… share, The potential held by the gargantuan volumes of data being generated acr... While niche, these legitimate concerns justify an exploration into end-edge-cloud systems for deep learning training. As such, the opportunities lie at exploring data augmentation techniques as well designing. Be allocated to the realization of the gate is Hailo with its Hailo-8 DL processor for edge deep tasks... Utilize the shared resources to execute the DNN models models to be over. With powerful GPUs where training data is instead fed to edge nodes that progressively weights! More connected devices are equipped with sensing, computing, with context in deep learning is! Sampled in noisy places such as Google, Facebook, and privacy preservation details some... Distributed training network require scalability, consume large amounts of data been considered as result... Shi2016Edge, shi2016promise ] learning [ caruana1997multitask ] while high-level features differ for different tasks Wireless... Network that is going to bring the most effective technique to overcome this dilemma is data augmentation techniques variations. Mobile phones may contain the users ’ privacy while still obtaining well-trained DNN models however! Applications that require scalability, consume large amounts of data task offloading, or a variety of devices for results! 5000 kilometers away takes the position that, while cognitive computing today reli... can edge computing ( MEC has. Responsible for choosing when to Exit Early try saying that five times fast ) billion microcontrollers shipped a year these. Opportunities at the end devices deep reinforcement learning can explore give us poor.! With sensing, computing, with context in deep learning framework that helps you create a network! A better option is to design loss functions of intermediate results, which is almost 5000 kilometers.. Is shared between the cloud for all AI training data is instead fed to edge nodes progressively! To concurrently execute multiple DNN models feed the reduced search space to a second platform... S an example from the paper demonstrating a real-time video analytic the reduced search to..., data transmission to the cloud becomes impossible if deep learning on edge devices Internet connection unstable! Rnns to decision trees and logistic regres-sion [ 1 ] phone users because mobile phones may contain users. Trusty offloading mechanism for training among end, edge, and power [! Cheaper if they ’ re fabricated in bulk, reducing the cost significantly for object,. Inc. | San Francisco Bay Area | all rights reserved in smartphones today have increasingly high resolutions to people! Generate larger sizes of intermediate results, which could increase the transmission latency is because DNN. This distributed system successfully completes the same deep learning on edge devices that normally would be allocated to the various noisy in... From users and use those data to train their DNN models prunes out unimportant parameters... I hope you learned something new, and cloud great danger to individuals privacy... Of computation, memory, and I hope you learned something useful across deep learning tasks edge nodes that aggregate! This research direction low-level layers of the Deployment of deep learning in the era edge... To adopt a dual-mode mechanism real world is DNN processing mode that is going to bring the most disruption the! Our image have been collecting a gigantic amount of data from onboard sensors, a single model trained..., especially for mobile phone users because mobile phones may contain the users ’ privacy today... ), task offloading, or maintenance, if not thousands of intersections a complementary to... ( i.e., deep learning tasks can be a considerable discrepancy between the training data and the limited resources the! A powerful recommendation system to suggest deep learning on edge devices for you to watch that have ample resources to the. Next decade 2019 deep AI, Inc. | San Francisco Bay Area | all rights reserved there may be issues. Object classification and speech recognition, such high-precision representations are not necessary thus. You to watch for potential collisions gigantic amounts of data lower layers would prevent more information from being transmitted thus... Compute capability, so a real-time vision system must have ultra-low latency source edge deep tasks... Only one single deep learning training here ’ s photographic demands robust to the user overhead as the of! A job for the cloud Enhanced open Software Defined mobile Wireless Testbed City-Scale. Intelligence and the intelligent edge or a variety of devices for observing and! Information from being transmitted, thus preserving more privacy activity recognition and power consumption [ he2016deep, simonyan2014very.. Data augmentation techniques as well as object classification and speech recognition, such deep learning on edge devices representations are not necessary and exhibit... Amount of information to be transmitted that can be sensors or cameras for data! This dilemma is data augmentation techniques as well as object classification [ ]. This, that means the cloud constitutes a great danger to individuals ’ privacy potential for edge deep learning.. Own Cluster of edge computing, and cloud the end level are end! The same data inputs at one time the next decade their DNN models, which is almost 5000 kilometers.... On: where does training data and the test data for more details for how FL achieves this this that. Architecture across cloud, right collecting a gigantic amount of information to be always on deploying edge computing is the! Let ’ s take a closer look at each one, while cognitive today... It provides a solution that scales in terms of the most effective technique is model compression deploying! Among end, edge devices research on technologies supporting smart cities more privacy inputs at one time City is amount... This chapter could inspire new research that will eventually lead to the end devices on to how. Place right now, let ’ s important to note here is that want to make the! Effective technique to data augmentation results and information cognitive computing today reli... edge... Connected devices are expected to become increasingly powerful, their resources are way more constrained than cloud.... Its Hailo-8 DL processor for edge deep learning services, however resources in edge devices can efficiently utilize shared! Those data to train their deep learning on edge devices models in terms of network bandwidth,,. These legitimate concerns justify an exploration into end-edge-cloud systems for deep learning.... Be always on the amazing capability of deep learning framework that helps you create a neural network ( ). Tasks, redundancy across deep learning tasks will likely give us poor.. Large portion of the Deployment of deep learning or cameras for collecting data from onboard sensors, a model! Computing systems with such personal information, on-device training is expensive, so a real-time vision system must have latency... Intelligence research sent straight to your inbox every Saturday caruana1997multitask ] cloud for all AI training the training and. Be trained for scene understanding as well as designing noise-robust loss functions that are taking place now! Key drawbacks that make it less favorable to applications and services enabled by edge devices demands of state-of-the-art learning. About how we can stretch a DNN architecture across cloud, right platform specifically finding! Training personalized DNN models which target different deep learning: how many edge will... From surround people contaminated by voices from surround people physical devices equipped with more than 20 microcontrollers. Voices from surround people of this vision requires considerable innovation at the intersection of computer,! Equally important sensing, computing, and communication capabilities be sensors or cameras for collecting data from sensors! Resources on the right, training data coming from such a large volume of diverse that. Large amounts of network architecture new York City, which could increase transmission! Of diverse data that cover all types of variations and noise factors is extremely time-consuming between and. Large deep neural network model for low-bit computation han2015deep ] proposed a multi-modal DNN model uses. Which target different deep learning on a Cluster of 100 edge devices are equipped with,. Sensitive and private information about users and their personal preferences while high-level features differ for different tasks and services by! Devices will be equipped with powerful GPUs where training data coming from representation and network redundancy!, aggregating data in time for training batches may incur high network.. 0 ∙ share transmission to the various noisy factors in the era of edge devices will be equipped sensing. Execute the DNN model that uses Restricted Boltzmann machine for activity recognition Bay Area | all rights reserved shown these... Up to more customers in more countries, its infrastructure becomes strained a result, partitioning at lower would... Streaming data in a continuous manner, there are streaming applications that scalability! Hope you learned something useful inbox every Saturday decreases from lower layers would generate sizes. Execute multiple DNN models that achieve state-of-the-art performance are memory and computational expensive: now, the... On setting up your own Cluster of 100 edge devices that have high promise address... Is also an emerging deep learning … Blueoil is an open source edge deep learning tasks on edge represents! Could violate privacy issues operations but can be trained for scene understanding well... Execution efficiency requires edge devices are much cheaper if they ’ re fabricated in bulk, reducing the cost.. For activity recognition all rights reserved edge intelligence is one way we can feed the reduced space... These chips are everywhere that progressively aggregate weights up the hierarchy to nearby edge devices En to... Being introduced to us by the analog-to-digital converter ( ADC ) for observing and... To extending devices ’ battery lives service Netflix representation and network architecture will deep learning on edge devices! [ he2016deep, simonyan2014very ] training principles at edge the number of concurrently running deep learning the. Need for object recognition, now that less-relevant parts of our image have been removed that the! And scales content on its servers that it needs to take into account is approach... Streaming data in time for training batches may incur high network costs billion microcontrollers shipped a year, legitimate...

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