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Industrial
Industrial machine vision and AI
Build smart cameras, frame grabbers, and vision controllers with flexible sensor interfaces, real-time processing, and AI acceleration.
Industrial vision at the edge
Machine vision is advancing with higher resolutions, faster frame rates, new interfaces, and growing AI adoption. Modern systems must handle image signal processing, format conversion, analytics, and high-speed data transport.
Altera FPGAs support diverse image sensors and interfaces, accelerate full ISP pipelines, and help deploy AI models in scalable smart cameras, frame grabbers, and vision controllers.
The Challenges, and Why FPGA
The Challenge
Higher resolution and frame rates
Vision cameras generate more pixels and frames, increasing bandwidth and processing demands.
Why FPGA
Parallel image processing
FPGAs process image streams in parallel for predictable throughput and low latency.
The Challenge
Complex ISP pipelines
Vision systems need correction, enhancement, format conversion, and analytics before data reaches AI or control.
Why FPGA
Programmable ISP pipeline
Implement and customize ISP functions in hardware as image sensors and quality requirements evolve.
The Challenge
Distributed AI data movement
AI vision systems move high-volume image data among cameras, frame grabbers, accelerators, and compute nodes.
Why FPGA
Efficient AI acceleration
Use FPGA AI Suite and high-speed interfaces to move, preprocess, and accelerate selected AI workloads.
Why Altera
FPGA AI Suite
Convert and optimize models from common AI frameworks, then integrate inference IP into FPGA designs.
Video and Vision Processing IP
Use IP for ISP, format conversion, analytics, HDR, genlock, and other video-processing functions.
High-speed vision interfaces
Connect cameras and frame grabbers with CoaXPress-over-Fiber and RoCEv2 for low-latency image transfer.
Critical Link Altera-based SOMs
Evaluate Agilex 5-based SOMs and Holoscan Sensor Bridge solutions for embedded vision designs.
Get Started
More Industrial Applications
Programmable Logic Controllers (PLCs)
Combine control logic, I/O, and industrial communications.
Semiconductor manufacturing equipment
Support precision control and inspection data processing.
Smart energy
Enable intelligent energy systems with real-time monitoring, efficient power management, and adaptable FPGA solutions for modern smart grids.
Common questions
An ISP pipeline conditions camera data through functions such as pixel correction, color processing and noise reduction. An AI model performs supported learned analysis on that prepared data; validate both image quality and model accuracy together.
Use CoaXPress-over-Fiber when supported camera and receiver endpoints, reach and bandwidth fit the installation. A local interface may suit short board-level connections; compare synchronization, cabling and available IP before choosing the transport.
Calculate aggregate pixel rates, processing stages, inference demand and memory traffic for simultaneous cameras. Include transport overhead and buffering, then validate the compiled design's resources and sustained throughput against the actual inspection rate.
RoCEv2 can move supported image data between compatible endpoints with reduced host transfer work. It needs suitable NICs, buffers and network behavior; check congestion, synchronization and system-level latency for the inspection topology.
Compare the exact model inputs against the validated software pipeline, including color order, scaling, cropping and numerical precision. Check converted-model results separately so transport loss or preprocessing changes are not mistaken for inference faults.
8b/10b encoding provides bounded disparity and transitions that aid serial communication, with detectable invalid code patterns. It adds coding overhead and does not replace electrical channel design or provide general error correction.