Future & Evolution of Edge AI
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Future & Evolution of Edge AI

Dear Friends,

The future of Edge AI is very bright. The market for Edge AI is expected to grow at a CAGR of 21.0% from 2023 to 2030, reaching a value of USD 72.6 billion by 2030. This growth is being driven by the increasing adoption of IoT devices, the need for real-time data processing, and the rising demand for security and privacy.

Here are some of the key trends that are expected to shape the future of Edge AI:

  • The growth of IoT devices: The number of IoT devices is expected to reach 50 billion by 2025. This will generate a massive amount of data that needs to be processed in real time. Edge AI can help to process this data locally, without having to send it to the cloud. This can improve the performance and efficiency of IoT applications.
  • The need for real-time data processing: Many applications require real-time data processing, such as self-driving cars, industrial automation, and medical diagnosis. Edge AI can help to process this data locally, without having to send it to the cloud. This can improve the responsiveness and safety of these applications.
  • The rising demand for security and privacy: Edge AI can help to improve the security and privacy of data by processing it locally. This can help to protect sensitive data from unauthorized access and cyberattacks.

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In addition to these trends, there are a number of other factors that are expected to drive the growth of Edge AI. These include the development of more powerful edge devices, the availability of low-cost edge computing solutions, and the increasing availability of open source Edge AI frameworks.

Overall, the future of Edge AI is very bright. Edge AI is a promising technology that has the potential to revolutionize a wide range of industries. As the technology continues to mature, we can expect to see even more innovative and groundbreaking applications of Edge AI in the years to come.

Here are some specific examples of how Edge AI is being used today and how it is expected to be used in the future:

  • Self-driving cars: Edge AI is being used in self-driving cars to help them navigate roads, avoid obstacles, and make decisions in real time.
  • Industrial automation: Edge AI is being used in industrial automation to monitor machines, detect defects, and optimize production processes.
  • Medical diagnosis: Edge AI is being used in medical diagnosis to help doctors identify diseases, make treatment decisions, and improve patient care.
  • Smart cities: Edge AI is being used in smart cities to improve traffic management, energy efficiency, and public safety.
  • Security: Edge AI is being used in security to detect intrusions, prevent cyberattacks, and protect critical infrastructure.

These are just a few examples of the many ways that Edge AI is being used today. As the technology continues to mature, we can expect to see even more innovative and groundbreaking applications of Edge AI in the years to come.

Your inputs, thoughts and views are most welcome!

Hassan Imani

Assistant Professor of Computer Engineering @ Bahcesehir University | PhD in Computer Engineering

12 个月

Thank you for your useful post. However, it seems that there is currently more attention on LLMs and cloud side processings. I am curious about your perspective on how edge AI will integrate with LLMs, particularly given the increasing focus on powerful GPUs and related technologies. Specifically, I am interested in understanding where computations will occur for tasks such as real-time object detection using cameras. Perhaps edge devices could be utilized in areas without access to the cloud for such computations.

Jesse Chapman

Verification Engineer

1 年

Yes edge AI is going to take off and it is much needed. There is companies at the “far edge” where you can process in real time on the sensor, without needing to go to the cloud. #Neuromorphic computing is going to drive the AI revolution. BrainChip is leading the wet with their neuromorphic approach and are the worlds first & only commercially available neuromorphic IP, with Renesas Electronics bringing out MCU’s containing their IP later this year followed by Valeo for their SCALA3 Lidar. Tata Elxsi and BrainChip partnered this week for medical and industrial applications.

Sreenivas Nandam

AI Architect | Advisor | MIT Technology Leadership

1 年

Yes, I agree. With C transformers, LLMs come to x86 CPUs allowing generative AI apps to run on edge devices like laptops. This coupled with LLMs of diff sizes from 7B to 70B enabled users to choose models relevant to application complexity. Domain specificity will grow exponentially now.

CHESTER SWANSON SR.

Realtor Associate @ Next Trend Realty LLC | HAR REALTOR, IRS Tax Preparer

1 年

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