Edge Imaging Webinar: Overcoming Bandwidth Limits at the Edge

Edge Imaging: Overcoming Bandwidth Limitations at the Edge

Edge Imaging allows high-resolution camera data to be acquired and processed close to the sensor instead of transferring the full imaging workload to a remote server or cloud system.

As camera resolutions and frame rates increase, however, embedded and edge computers can face significant bandwidth, processing, and storage limitations.

In this webinar, Gidel Founder and CTO Reuven Weintraub explains how FPGA-based acquisition, processing, and compression can help overcome these limitations while maintaining deterministic real-time operation.

Why High-Resolution Edge Imaging Creates Bandwidth Challenges

Modern imaging systems generate increasingly large data streams as camera resolution, frame rate, bit depth, and camera count increase.

When an embedded CPU or GPU receives these streams directly, acquisition can consume significant system bandwidth and processing resources before the application or AI workload even begins.

This challenge becomes especially important in compact embedded systems, where PCIe bandwidth, memory bandwidth, storage capacity, network throughput, power, and thermal limits must all be considered.

FPGA Acquisition for High-Bandwidth Vision

Gidel uses FPGA-based image acquisition to handle high-bandwidth camera streams before they reach the embedded processor.

The FPGA can manage deterministic acquisition, protocol processing, timing, buffering, image preprocessing, and data preparation within the acquisition pipeline.

This allows the embedded CPU or GPU to focus more of its resources on AI inference, application processing, control, and other system-level tasks.

FantoVision Edge AI Systems for Imaging at the Edge

Gidel’s FantoVision Edge AI Systems combine NVIDIA Jetson computing with FPGA-based image acquisition and processing in compact embedded platforms.

FantoVision supports high-bandwidth camera interfaces including GigE Vision, CoaXPress, and Camera Link. The FPGA handles acquisition and pixel-intensive processing before transferring image data to the Jetson for AI and application processing.

This heterogeneous FPGA and Jetson architecture supports embedded vision applications that require high data rates, low latency, deterministic acquisition, and real-time AI processing.

Real-Time Compression Reduces Imaging Data

Image compression can reduce the amount of data that downstream processors, storage systems, and networks must handle.

Gidel’s FPGA Image Compression IPs operate directly within the FPGA processing pipeline.

As a result, the FPGA can compress image data during acquisition before the full uncompressed stream moves deeper into the edge computing architecture.

For applications that require high image quality with significant data reduction, Gidel’s Quality+ Compression can further reduce bandwidth and storage requirements while maintaining high image quality.

Multiple Camera Interfaces for Embedded Vision

Different edge imaging applications require different camera interfaces depending on bandwidth, cabling, distance, synchronization, and system architecture.

In addition, Gidel supports embedded and edge imaging systems using GigE Vision, CoaXPress, and Camera Link acquisition.

The platform can acquire directly from high-bandwidth CoaXPress cameras, GigE Vision cameras, and Camera Link cameras. This allows FPGA-based acquisition and processing to match different bandwidth, cabling, and system requirements.

Applications for High-Bandwidth Imaging at the Edge

• Embedded AI and machine vision
• Industrial inspection and automation
• Multi-camera imaging systems
• High-resolution image acquisition
• Real-time recording and streaming
• Edge AI detection, tracking, and classification
• Compact and SWaP-constrained vision systems

Explore Gidel Edge Imaging Platforms

• FantoVision Edge AI Systems
• PCIe Frame Grabbers
• Mini FPGA Modules

Watch the full Edge Imaging webinar above to learn how Gidel combines FPGA acquisition, processing, compression, and embedded computing to overcome high-bandwidth imaging challenges at the edge.

For more technical videos, visit the Gidel YouTube Channel.