Exploring edge AI: Insights from Brainchip’s 2023 edge AI technology report
The recently published 2023 Edge AI Technology Report delves into the present landscape of edge AI, showcasing its diverse applications in both the industrial and consumer realms.
The report, sponsored by Brainchip, Arduino, Arm, Edge Impulse and Sparkfun, explores the advantages of this technology and also sheds light on the challenges it faces. According to experts, edge AI is transforming multiple industries, such as industrial manufacturing, healthcare, consumer products, transportation, smart cities and smart homes.
The edge AI report explores how platforms such as TensorFlow Lite, PyTorch Mobile, OpenVino, Nvidia Jetson and others support edge AI development. It highlights critical hardware and software selection considerations, including integration and security, to ensure a smooth and secure implementation of edge AI.
TinyML is also featured prominently in the report, exploring its advantages, development tools and potential challenges. It delves into the role of TinyML in the products of the future.
The report also examines various edge AI algorithms, including classification, detection, segmentation and tracking algorithms. It further explores the harmonizing of these algorithms with hardware.
Sensing modalities, including vision-based, audio-based and environmental sensing, are also discussed alongside data collection methods. The report showcases how companies like Sparkfun are simplifying data logging, for example.
In a series of case studies, the report provides glimpses into the real-world applications of edge AI. Companies like ST, Sensory Inc and Pachama demonstrate how they’re transforming industries with edge AI technologies.
The report also addresses the challenges of edge AI, such as data management, integration, security, latency, scalability, cost and power consumption. It also suggests potential solutions to these issues.
Looking to the future, the report explores the potential impact of the emergence and growth of 5G/6G networks on edge AI. It foresees a growing significance of neuromorphic computing and event-based processing. Additionally, the report emphasizes the importance of data-efficient AI and in-memory computing for edge AI.
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Article Topics
AI/ML | ARM | Brainchip | chip | edge AI | Edge Impulse | PyTorch | Tensorflow | TinyML
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