Make AI faster, more cost-effective at the edge

Implement advanced memory and storage architectures that reduce model retraining time and improve inferencing accuracy. This way, you can accelerate critical edge AI workloads like NLP, predictions, personalization, and computer vision.

Edge AI use cases are chosen to optimize GPU usage, data egress, and power consumption. Examples include:
 

• Smart retail: Analyze customer behavior, manage inventory, and personalize shopping experience
• Computer vision: Gain real-time processing and low latency for computer vision workloads
• Predictive maintenance: Monitor devices to help prevent equipment failures and minimize downtime
• NLP: Enhance interactions between humans and machines with real-time inferencing

Latency: For some workloads, moving to the edge can reduce latency, which in turn can improve customer experiences, make safer work environments, decrease downtime, and provide real-time insights. Other workloads don’t rely as heavily on low-latency performance, making them more suitable for the cloud.

Data transport: Cloud bills can skyrocket if the volume of data transport gets too high. Edge AI can reduce the strain by processing most of the data locally, and only transferring the essentials to the cloud. With this strategy, you can reduce the requirements and congestion of your network.

Resource efficiency: Lightweight workloads can often be moved to the edge to run more efficiently. At the same time, deploying edge AI devices can be costly, leading to compromises about how to balance performance and efficiency.

Security: Cloud systems can provide suitable security for a range of workloads. However, there are some situations where edge servers provide a necessary extra layer of security to comply with security regulations.

In regions where data sovereignty laws dictate that data must remain within national borders, edge computing may be a legal obligation.

Processing and storing data locally helps you stay compliant with regulatory requirements while implementing new AI applications. This is particularly important in industries like finance and healthcare, where data integrity can have major ramifications.

Collaborate with Micron’s ecosystem experts to develop a cloud-to-edge strategy that harnesses the power of your data, wherever it lives. Micron rigorously tests and optimizes AI workloads across diverse platforms, ensuring seamless performance and scalability for AI-powered edge applications. We also work closely with customers at engineering sites across the country to streamline processes and reduce the load on your engineering teams.