Right-Sizing Your Edge AI Hardware
(Room Ballroom A)
05 Nov 25
3:50 PM
-
4:15 PM
Tracks:
Embedded AI: Architectures & Applications
In the rapidly evolving landscape of edge AI, particularly within the computer vision space, the selecting the right hardware is a critical decision. Many AI solutions are deployed on over-specced and underutilized hardware, leading to unnecessarily high costs that can compound quickly as deployments scale. This session will provide a practical guide to "right-sizing" your edge AI hardware, ensuring optimal performance without breaking the bank.
We will delve into the key trade-offs between AI model size, accuracy, and inference speed, using real-world examples and benchmark data. You'll learn how to establish clear goals for your application, including latency and frame rate throughput, and how these goals should inform your hardware selection. We will explore the performance of various hardware options, from high-end discrete GPUs to more cost-effective integrated GPUs (iGPUs) and Neural Processing Units (NPUs).
Discover why a discrete GPU isn't always the answer and how, for many common use cases, integrated solutions can provide the required performance at a fraction of the cost. We'll also present a strategic approach to prototyping and scaling, starting with a flexible development environment and moving towards a cost-optimized solution for mass deployment.
Join us to learn how to make informed hardware choices that can lead to significant savings and ensure the success of your edge AI projects.