Differentiate and Optimize Your Machine Vision Innovations with AMD

Topics Covered

Adaptive SoCs for Embedded Vision and AI
Edge AI Architecture and Deployment Tradeoffs
Flexible Sensor Connectivity for Machine Vision
FPGA-Based High-Speed I/O and Data Processing
Low-Latency AI Inference at the Edge
Ryzen Embedded Processing for Power-Efficient AI

Overview:

Edge AI resources are most useful when they connect system constraints to the architectures that can support them. Avnet’s new edge AI hub brings together solution papers, market insights, and supplier partner content to help design engineers evaluate performance, latency, power, and deployment tradeoffs in production-ready systems.

One resource in the edge AI hub – AMD’s on-demand webinar, Differentiate and Optimize Your Machine Vision Innovations – focuses on machine vision and embedded AI systems.

In the webinar, engineers will learn how AMD solutions support:

Flexible sensor connectivity for vision-based systems
Low-latency and deterministic data throughput
Low-latency AI inference in compact form factors
FPGA solutions for high I/O and serial I/O performance
Adaptive SoC products for embedded vision and AI workloads
Ryzen™ Embedded processors for edge computing and power-efficient AI

The session also covers design paths for factory automation cameras, frame grabbers, AI vision cameras, and embedded AI boxes.

For teams developing machine vision systems, the webinar offers practical context for choosing architectures that support responsiveness, sensor integration, and edge AI performance.

Thank You For Your Interest

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