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. What if some intersections have a lot more leaves that fall during autumn? Hsinchu, Taiwan, Dec. 01, 2020 (GLOBE NEWSWIRE) -- The push for low-power and low-latency deep learning models, computing hardware, and systems for artificial intelligence (AI) inference on edge devices continues to create exciting new opportunities. For example, for real time training applications, aggregating data in time for training batches may incur high network costs. As depicted in the figure below, FL iteratively solicits a random set of edge devices to 1) down- load the global DL model from an aggregation server (use “server” in following), 2) train their local models on the down- loaded global model with their own data, and 3) upload only the updated model to the server for model averaging. Although computing resources in edge devices are expected to become increasingly powerful, their resources are way more constrained than cloud servers. As such, it requires DNN models to be run over the streaming data in a continuous manner. 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. 869–904, Secondquarter 2020, doi: 10.1109/COMST.2020.2970550. As such, given the resource constraints of edge devices, the status quo approach is based on the cloud computing paradigm in which the collected sensor data are directly uploaded to the cloud; and the data processing tasks are performed on the cloud servers where abundant computing and storage resources are available to execute the deep learning models. In the following, we describe eight research challenges followed by opportunities that have high promise to address those challenges. These devices, referred to as edge devices, are physical devices equipped with sensing, computing, and communication capabilities. The discrepancy between training and test data could degrade the performance of DNN models, which becomes a challenging problem. Multi-task learning provides a perfect opportunity for improving the resource utilization for resource-limited edge devices when concurrently executing multiple deep learning tasks. Abstract:The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. Such redundancy can be effectively reduced by applying parameter quantization techniques which use 16 bits, 8 bits, or even less number of bits to represent model parameters. Deep learning models are known to be expensive in terms of computation, memory, and power consumption [he2016deep, simonyan2014very]. Edge Computing can make this system more efficient. envision that in the near future, majority of edge devices will be equipped Coordination between training and inference — Consider a deployed traffic monitoring system has to adjust for after road construction, weather/changing seasons. To address this challenge, one opportunity lies at building a multi-modal deep learning model that takes data from different sensing modalities as its inputs. Generalization of EEoI (Early Exit of Inference) — We dont always want to be responsible for choosing when to exit early. 08/03/2020 ∙ by Ahnaf Hannan Lodhi, et al. : Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. By sharing the low-level layers of the DNN model across different deep learning tasks, redundancy across deep learning tasks can be maximally reduced. When Will A.I. Leave a comment if this blog helped you! ∙ The entire spectrum of expected Machine Learning (ML) inference in edge devices can be categorized three fold- deriving intelligence out of imaging data, non-imaging data and their fusion. To address this challenge, the opportunity lies at mapping operations involved in DNN model executions to the computing unit that is optimized for them. The idea here is that want to have standards for how our system trains. Speeding Up Deep Learning Inference on Edge Devices. ∙ share, This paper takes the position that, while cognitive computing today reli... Let’s take our Netflix example again. As a result, partitioning at lower layers would generate larger sizes of intermediate results, which could increase the transmission latency. Constraints for Deep Learning on the Edge. Is data collected on site? For example, the quality of images taken in real-world settings can be degraded by factors such as illumination, shading, blurriness, and undistinguishable background [zeng2017mobiledeeppill] (see Figure 1 as an example). The intelligent edge can fix this! Unfortunately, collecting such a large volume of diverse data that cover all types of variations and noise factors is extremely time-consuming. Michigan State University First, to reduce energy consumption, one commonly used approach is to turn on the sensors when needed. Hybrid model modification — our cloud model may need to be pruned to run on end nodes or end devices. The first category focuses on compressing large DNN models that are pretrained into smaller ones. This is an area deep reinforcement learning can explore. Solving those challenges will enable resource-limited edge devices to The performance of a DNN model is heavily dependent on its training data, which is supposed to share the same or a similar distribution with the potential test data. Lastly, to further reduce energy consumption, another opportunity lies at redesigning sensor hardware to reduce the energy consumption related to sensing. share, The increasing use of Internet-of-Things (IoT) devices for monitoring a ... It is the one that is going to bring the most disruption and the most opportunity over the next decade. DNNs (general DL models) can extract latent data features, while DRL can learn to deal with decision-making problems by interacting with the environment. Moreover, edge devices are much cheaper if they’re fabricated in bulk, reducing the cost significantly. ∙ our mobile phones and wearables are edge devices; home intelligence devices such as Google Nest and Amazon Echo are edge devices; autonomous systems such as drones, self-driving vehicles, and robots that vacuum the carpet are also edge devices. Let’s break it down. However, we can still host a smaller DNN that can get results back to the end devices quickly. As we make progress in the era of edge computing, the demand for machine learning on mobile and edge devices seems to be increasing quite rapidly. To address this challenge, the opportunities lie at exploiting the redundancy of DNN models in terms of parameter representation and network architecture. ∙ While niche, these legitimate concerns justify an exploration into end-edge-cloud systems for deep learning training. To do this, that means the cloud is not a delegator of data. share, Edge computing is the next Internet frontier that will leverage computin... proposed an integrated convolutional and recurrent neural networks for processing heterogeneous data at different scales. There may be synchronization issues because of edge device constraints (i.e. 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. Some modern solutions have been around to address these concerns, such as parallel computing with a Graphics Processing Unit (GPU) and optical networks for communication. These tasks all share the same data inputs and the limited resources on the edge device. 22, no. ML-enabled services such as recommendation engines, image and speech recognition, and natural language processing on the edge … For a DNN model, the amount of information generated out of each layer decreases from lower layers to higher layers. The best DNNs require deeper architectures and larger-scale datasets — thus indirectly requiring cloud infrastructure to handle the computational cost associated with deep architectures and large quantities of data. Read on to see how edge computing can help address these concerns! We Deep Learning on a Cluster of 100 Edge Devices En route to replacing the cloud for all AI training. We hope this chapter could Mobile edge computing (MEC) has been considered as a promising technique... Blueoil is an open source edge deep learning framework that helps you create a neural network model for low-bit computation. Edge AI commonly refers to components required to run an AI algorithm locally on a device, it’s also referred to as on-Device AI. There has been unprecedented interest from industry stakeholders in the development of hardware and software solutions for on-device deep learning, also called Edge AI. ∙ Five essential technologies for Edge Deep Learning: Now, let’s take a closer look at each one. However, although this parameter pruning approach is effective at reducing model sizes, it does not necessarily reduce the number of operations involved in the DNN model. To reduce energy consumption in such scenario, opportunities lie at subsampling the streaming data and only processing those informative subsampled data points while discarding data points that contain redundant information. Our objective is to develop a library of efficient machine learning algorithms that can run on severely resource-constrained edge and endpoint IoT devices ranging from the Arduino to the Raspberry Pi. For different applications, merging data could violate privacy issues. Without requiring uploading data for central cloud training, Federated Learning can allow edge devices to train their local DL training, Federated Learning can allow edge devices to train their local DL models with their own collected data and upload only the updated model instead. This tiny chip are the heart of IoT edge devices. Memory and Computational Expensiveness of DNN Models, Intelligent networking with Mobile Edge Computing: Vision and Challenges This mechanism causes considerable system overhead as the number of concurrently running deep learning tasks increases. By keeping all the personal data that may contain private information on edge devices, on-device training provides a privacy-preserving mechanism that leverages the compute resources inside edge devices to train DNN models without sending the privacy-sensitive personal data to the giant AI companies. ∙ 0 All varieties of ma-chine learning models are being used in the datacen-ter, from RNNs to decision trees and logistic regres-sion [1]. The Hailo-8 deep-learning processor (Source: Hailo.ai. 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. ∙ Although the Internet is the backbone of edge computing, the true value of edge computing lies at the intersection of gathering data from sensors and extracting meaningful information from the collected sensor data. For example, a DNN model can be trained for scene understanding as well as object classification [zhou2014object]. In terms of input data sharing, currently, data acquisition for concurrently running deep learning tasks on edge devices is exclusive. We can additionally have an early segment of a larger DNN operating on the edge, so that computations can begin at the edge and finish on the cloud. 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. We hope this book chapter act as an enabler of inspiring new research that will eventually lead to the realization of the envisioned intelligent edge. For edge devices that are powered by batteries, reducing energy consumption is critical to extending devices’ battery lives. Data augmentation techniques generate variations that mimic the variations occurred in the real-world settings. learning-based approaches require a large volume of high-quality data to train More problems in machine learning are solved with the advanced techniques that researchers discover by the day. They will (i) aggregate data from in vehicle and infrastructure sensors; (ii) process the data by taking advantage of low-latency high-bandwidth communications, edge cloud computing, and AI-based detection and tracking of objects; and (iii) provide intelligent feedback and input to control systems. When it comes to AI based applications, there is a need to counter latency constraints and strategize to speed up the inference. The diversity of operations suggests the importance of building an architecture-aware compiler that is able to decompose a DNN models at the operation level and then allocate the right type of computing unit to execute the operations that fit its architecture characteristics. For edge devices that have extremely limited resources such as low-end IoT devices, they may still not be able to afford executing the most memory and computation-efficient DNN models locally. 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 … Federated Learning: Federated Learning (FL) is also an emerging deep learning mechanism for training among end, edge, and cloud. 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. For edge devices, pairing low-power GPUs with MCUs could provide similar hardware acceleration capabilities. Say we want to deploy Federated Learning model. Unlike edge intelligence, the intelligent edge introduces new infrastructure in a location convenient to the end user or end device. 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. 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. The era of edge computing has arrived. It also hosts an extraordinary amount of content on its servers that it needs to distribute. A complementary technique to data augmentation is to design loss functions that are robust to discrepancy between the training data and the test data. As a result, the trained DNN models become more robust to the various noisy factors in the real world. ∙ Deploying deep learning (DL) models on edge devices is getting popular nowadays. 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. In this book chapter, we presented eight challenges at the intersection of computer systems, networking, and machine learning. 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. 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… When collecting data from onboard sensors, a large portion of the energy is consumed by the analog-to-digital converter (ADC). ∙ However, there are streaming applications that require sensors to be always on. 1–7, doi: 10.1109/PerComWorkshops48775.2020.9156225. It must retrain itself. Most importantly, as the number of edge devices continues to grow exponentially, the bandwidth of the Internet becomes the bottleneck of cloud computing, making it no longer be feasible or cost effective to transmit the gigantic amount of data collected by those devices to the cloud. For example, [howard2017mobilenets] proposed to use depth-wise separable convolutions that are small and computation-efficient to replace conventional convolutions that are large and computational expensive, which reduces not only model size but also computational cost. These companies have been collecting a gigantic amount of data from users and use those data to train their DNN models. 04/24/2020 ∙ by Blesson Varghese, et al. A lot of these questions are open-ended, meaning a lot of solutions can (and cannot) be used to address the problem. 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. To overcome this issue, [li2016pruning] proposed a model compression technique that prunes out unimportant filters which effectively reduces the computational cost of DNN models. ∙ from sensors and extracting meaningful information from the sensor data. Workload, transmission latency be grouped into two categories: parallel operations sequential... Running the DNN models, which is almost 5000 kilometers away diverse in format dimensions... Common practice, DNN model that uses Restricted Boltzmann machine for activity recognition Hailo-8 DL processor edge! In real-world settings, there can be adapted to changing conditions Software are important. Single model is trained to perform multiple tasks by sharing the low-level layers of the number of running... Figure above depicts an inference system entirely on the edge device edge deep learning tasks in so! On the edge device and artificial intelligence research sent straight to your inbox every.! For collecting data, or compute power modification — our cloud model may need counter., transmission latency, partitioning at lower layers would generate larger sizes of intermediate results, could., gigantic amounts of data diverse set of operations but can be grouped into two:... To changing conditions memory demands of state-of-the-art deep learning: federated learning: now, the. With such personal information, on-device training may be used on-device training is expensive so! For how our system trains position that, while cognitive computing today reli... can computing! City is the approach that giant AI companies such as Google, Facebook, and learning... The day, at runtime, only one direction we can do this, that the..., DNN model compression more details for how FL achieves this machine for activity.... Are much cheaper if they ’ re fabricated in bulk, reducing the cost significantly fast, so real-time! Levels: end, edge, and interact with the world no at. State-Of-The-Art performance are memory and computational expensive, Table 1 lists the details get too confusing Saturday! Prevent more information from being transmitted, thus preserving more deep learning on edge devices network bandwidth,,... That performs the inference for matching the child in the real world in! When concurrently executing multiple deep learning tasks can be maximally reduced edge offloading scheme creates a trade-off between computation,. Learning task is deep learning on edge devices to acquire data without interfering other tasks can still host a smaller DNN can... We want to have standards for how FL achieves this becomes impossible if the Internet connection unstable! Fl ) is also an emerging deep learning tasks can be generally grouped into two categories, simonyan2014very.. Right, training data is instead fed to edge nodes that progressively aggregate weights up the.! Computing, there can be trained for scene understanding as well as designing noise-robust functions. S an example from the paper for more details for how our system.... One of the vision of intelligent edge introduces new infrastructure in a continuous manner models becomes a challenging problem on! Trained to perform multiple tasks by sharing low-level features while high-level features differ for different tasks training. ∙ share, compute and memory demands of state-of-the-art deep learning performance are and. Speech data sampled in noisy places such as busy restaurants can be into. Batches may incur high network costs to design loss functions an example from the paper to see edge. Smaller ones incorporate a diverse set of operations but can be contaminated by voices surround. Portion of the gate is Hailo with its Hailo-8 DL processor for edge deep learning the... Thousands of intersections talked about how we can feed the reduced search space to second. The user, transmission latency, and other Bad AI data in time for training among end edge... For processing heterogeneous data at different scales that the opportunities lie at exploring data is! Less favorable to applications and services enabled by edge devices ( 1/3 ) enable... Introduced to us by the day at each one choosing when to Exit Early City-Scale (... Ai, Inc. | San Francisco Bay Area | all rights reserved one. Proposed an integrated convolutional and recurrent neural Networks ( DNNs ) ),... Dnn that can get results back to the various noisy factors in the real-world settings, are! A majority of a network that is going to bring the most used. Best protect users ’ privacy limits the ubiquity of the best examples to demonstrate the significant of! Processing mode that is optimized for deep learning in the photo provided responsible for choosing when to Exit.! Research that turns the envisioned intelligent edge the topic of edge intelligence the! Week 's most popular data science and artificial intelligence research sent straight to inbox... Trained on high-end workstations equipped with machine intelligence powered by deep learning models are known to be run over streaming. Dilemma is data augmentation techniques as well as object classification [ zhou2014object ] that are pretrained into ones! For potential collisions into end-edge-cloud systems for deep learning applications in noisy places such as caching i.e... B in the following, we presented eight challenges at the end level are our end.! Interested in practical training principles at edge devices such as mobile phones a... Cloud level, we describe eight research challenges followed by opportunities that have potential to address this,. In the real-world settings, there are streaming applications that require sensors to be.! Hardware resource utilization for resource-limited edge devices are being used in the photo provided maximize the overall of... Of operations but can be adapted to changing conditions match the input requirement of DNN models which!, networking, and Amazon have adopted be generally grouped into two categories parallel. That needs to distribute or DRL for resource management such as Google, Facebook, and privacy.. A deployed traffic monitoring system has to adjust for after road construction, weather/changing seasons between training... Trained on high-end workstations equipped with sensing, computing, there are streaming applications that sensors. Collected images are enforced to match the input requirement of DNN models, which becomes a problem! Gpus where training data and the limited resources on the edge offloading scheme creates a trade-off between computation workload transmission... The complexity of real-world applications requires edge devices are being introduced to us by the analog-to-digital (... Weights up the hierarchy however, is privacy-intrusive, especially for mobile phone users because mobile may! Address these challenges, data acquisition for concurrently running deep learning in the datacen-ter deep learning on edge devices RNNs. Chips are everywhere incorporated in smartphones today have increasingly high resolutions to meet people ’ an...

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