Edge AI makes it possible for artificial intelligence algorithms to run locally, either on the device or on the server close to the device.
The edge AI market is forecasted to grow significantly through the 2020s, with a CAGR of north of 20%.
Improved privacy, security, latency, and load balancing are some of the innovations driving growth in the edge AI space.
What are examples of edge AI startups?
Myelin Foundry, Blaize, and Latent AI are trending edge AI startups.
What does edge AI do?
Edge AI is part of the TinyML trend.
Tiny machine learning is a technique that shrinks deep learning networks to fit into small hardware.
This can be used on edge devices like sensors and wearables.
What are some use cases for edge AI??
Edge AI can be used for a variety of tasks, including image recognition, video processing, natural language processing, and predictive maintenance.
Some edge AI use cases include:
- Monitoring and managing industrial equipment
- Autonomous vehicles
- Fraud detection
- Predictive maintenance
- IoT applications
What are the benefits of edge AI?
The benefits of edge AI include:
- improved privacy, as data never leaves the device
- improved security, as data is not shared with a third-party
- reduced latency, as there is no need to communicate with a remote server
- improved load balancing, as edge devices can share the workload with servers
What are the challenges of edge AI?
Some of the challenges of edge AI include:
- power consumption, as edge devices often have limited battery life
- data storage, as edge devices often have limited storage space
- processing power, as edge devices often have limited processing power.
Shipments of TinyML chipsets
Global shipments of TinyML chipsets are expected to reach 2.5 billion units by 2030.
This represents a 164x increase from its 2020 levels of 15.2 million units.
What are examples of TinyML startups?
Vivoka, Perceive, and Cornami are examples of trending TinyML startups.
Edge AI is a growing trend in the world of artificial intelligence.
This technology has the potential to improve privacy, security, latency, and load balancing.
TinyML is a related trend that is also growing in popularity.
One challenge of edge AI is data storage and processing at the edge, as devices can have limited resources. Another challenge is developing algorithms that can run on the edge without requiring extensive resources.