Edge AI: State-of-the-Art

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Aug 23, 2023 02:35 PM

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Egde-ai

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Public

Edge AI: State-of-the-Art

Edge AI refers to the process of running artificial intelligence (AI) algorithms and processing data on physical devices at the site of data collection, instead of relying on a centralized, cloud-based infrastructure. With the advent of Internet of Things (IoT) devices and 5G networks, edge AI technology has been evolving rapidly over the past few years, creating substantial shifts in a variety of industries and applications. This report primarily emphasizes the latest advancements in edge AI technology and the expanding trends shaping the field based on the review of available sources.

Decoupling AI from the Cloud

One of the most prominent trends in edge AI advancements is the shift toward separating AI from the cloud, allowing for localized AI processing on IoT devices (NVIDIA, 2023; IEEE, 2023; TechRepublic, 2023). Tech giants, Google and Arm, are pioneering this trend with advanced chip designs and software toolkits capable of facilitating AI processing directly at the edge, effectively bypassing the need for a cloud connection. This shift not only accelerates real-time processing but also substantially diminishes latency, making edge AI a more reliable and responsive alternative.

Emergence of Machine Learning Operations (MLOps)

MLOps, the process of deploying, monitoring, and updating machine learning models, has emerged as a vital practice in edge AI (NVIDIA, 2023; IEEE, 2023; TechRepublic, 2023). MLOps concerns itself with the extensive management of data flow towards the edge and ensures optimum performance via continuous AI model updates. With the increasingly complex demands in managing AI applications, MLOps is evolving as a compelling solution for organizations striving for a dependable operations routine.

Specialized Chips for Enhanced Processing

Recognizing the necessity for advanced processing power at the edge, many companies are developing specialized chips to meet the unique requirements of edge AI applications (NVIDIA, 2023; IEEE, 2023; TechRepublic, 2023). Startups such as DeepVision have developed distinctive chips for video analytics and natural language processing specifically designed for edge AI applications. This niche field has seen significant financial backing. Custom chip development paves the way for more robust and efficient edge processing, with projections estimating the presence of deep learning accelerators in approximately 1.9 billion edge devices by 2025 (NVIDIA, 2023).

Expansion of AI Use Cases & Capabilities

One of the most exciting trends in edge AI is the ongoing expansion of AI use cases and capabilities, notably in the field of computer vision (NVIDIA, 2021; NVIDIA, 2023; Windows, 2023; Microsoft, 2023; TechRepublic, 2023). While computer vision was previously occupied with object recognition, the burgeoning interest in multimodal AI allows for advancements beyond merely identifying figures to more granular feature recognition, leading to increased capabilities and heightened application utility. Industries like robotics, advertising, healthcare, and shopping are predicted to be primary beneficiaries of these breakthroughs.

The 5G Revolution: AI-on-5G

The proliferation of 5G networks has spawned another promising area of development: AI-on-5G (NVIDIA, 2021; Windows, 2023; Microsoft, 2023; TechRepublic, 2023). AI-on-5G amalgamates secure and high-performance computing infrastructure with connectivity, fostering unprecedented possibilities for edge AI integrations. With its keywords being ultra-low latency and enhanced security, AI-on-5G is anticipated to redefine various domains, including industrial actions and vehicle telemetry.

In conclusion, edge AI, as a field, is experiencing rapid, vibrant growth fueled by the proliferation of IoT devices, the needs of the 5G networks, and the demand for more efficient and streamlined business processes. From the decoupling of AI from the cloud, the rise of MLOps, and specialized chip developments, to an expansion of AI use cases, and the revolution of AI-on-5G, the current state-of-the-art technology is vigorous and expanding. However, as edge AI continues to evolve, there is an increasing need to address emerging security concerns, primarily because real-time data processing at the edge is more susceptible to cyberattacks.

References

NVIDIA (17 December 2021). Top 5 Edge AI Trends 2022. Available at https://blogs.nvidia.com/blog/2021/12/17/top-5-edge-ai-trends-2022/

Windows (23 May 2023). Microsoft Edge Build 2023. Available at https://blogs.windows.com/msedgedev/2023/05/23/microsoft-edge-build-2023-innovations-in-ai-productivity-management-sidebar-apps/

Microsoft (2023). Microsoft Edge Features. Available at https://www.microsoft.com/en-us/edge/features/ai

TechRepublic (2023). Edge AI trends. Available at https://www.techrepublic.com/article/edge-ai-trends/

NVIDIA (19 December 2023). Edge AI Trends 2023. Available at https://blogs.nvidia.com/blog/2022/12/19/edge-ai-trends-2023/

IEEE (2023). Autonomous AI Systems on IoT Edge Devices. Available at https://ieeexplore.ieee.org/document/9555813

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