What is Embedded Vision?
Embedded vision is the integration of image capture, processing, and analysis directly into a device, enabling machines to see and interpret visual data in real time, locally, without relying on the cloud. Unlike traditional machine vision systems that offload computation to remote servers, embedded vision refers to vision processing that takes place within a device, regardless of whether that processing runs on CPUs, GPUs, Field Programmable Gate Arrays (FPGAs), or system-on-chip (SoC) architectures.
By combining image sensors, specialized processors such as FPGAs, and optimized algorithms into compact, power-efficient form factors, embedded vision enables devices across industries, from factory robots to autonomous vehicles, to make intelligent decisions based on what they see, instantly and autonomously.
How Does an Embedded Vision System Work?
An embedded vision system consists of three core components working together: sensing, processing, and analysis.
Sensing captures environmental data. While cameras using interfaces like MIPI CSI-2 are most common, other modalities such as lidar and radar can also generate structured data representations suitable for environmental perception and analysis.
Processing converts raw sensor data into actionable information. A low power processing engine, often an FPGA, performs real-time image signal processing (ISP), format conversion, sensor aggregation, and AI/ML inferencing directly on the device. CPUs and GPUs also play important roles. GPUs, like FPGAs, are capable of parallel processing, though they differ in architecture and execution models. The choice of processor depends on the application’s performance, power, and flexibility requirements.
Analysis applies intelligent software and algorithms to the processed data. This includes both traditional computer vision techniques and machine learning models for tasks such as object detection, motion tracking, defect inspection, HDR, noise reduction, rotation, geometric correction, and de-warping.
Why Does Embedded Vision Matter at the Edge?
Technology trends including 5G, Industry 4.0, smart infrastructure, and the rise of physical AI are driving demand for vision systems that process data where it is generated, rather than transmitting massive video streams to distant cloud servers.
Processing vision locally delivers several critical advantages:
- Low latency: Decisions are made in milliseconds, with no round-trip to the cloud
- Bandwidth efficiency: Only processed results, not raw video, need to be transmitted
- Privacy: Sensitive visual data stays on the device
- Reliability: Operation continues without a network connection
- Power efficiency: Purpose-built hardware consumes far less energy than cloud offloading
These attributes are essential for robotics, industrial automation, and automotive systems.
Challenges to consider include limited on-device memory and processing resources, power and thermal constraints, and the complexity of optimizing AI models for edge deployment through techniques such as quantization and pruning.
Why Use an FPGA for Embedded Vision?
The primary advantage of FPGAs for embedded vision is parallel processing. Through a “sea of gates” architecture, FPGAs process multiple video streams and AI inference tasks simultaneously rather than sequentially, delivering deterministic, real-time performance at significantly lower power than CPUs or GPUs.
FPGAs are also reconfigurable. Unlike fixed-function ASICs, they can be reprogrammed in the field to adapt to evolving algorithms, new sensor interfaces, or updated AI models, extending product lifecycles and future-proofing designs. FPGAs natively support high-speed interfaces including MIPI CSI-2, PCIe, USB, LVDS, and Gigabit Ethernet, simplifying integration with diverse camera sensors and display ecosystems. Advanced FPGAs further include hardware-based security features and extremely low soft error rates, which are critical for automotive and industrial vision deployments. For embedded vision applications where both high compute performance and low power operation are required, such as edge AI, FPGAs deliver an ideal balance.
Where Is Embedded Vision Used?
Embedded vision is deployed across a rapidly expanding range of industries wherever visual intelligence is needed at the edge:
- Industrial automation: Quality inspection and defect detection on production lines
- Automotive systems: Lane departure warnings, pedestrian detection, driver monitoring
- Medical devices: Portable diagnostic imaging, endoscopy, wearable health monitors
- Security and surveillance: Edge-based facial recognition and anomaly detection
- Consumer electronics: Smartphones, smart cameras, AR/VR headsets
- Robotics: Real-time navigation, obstacle avoidance, manipulation
As the leading provider of low power FPGAs globally by volume, Lattice creates FPGAs that power embedded vision across all of these markets for tens of thousands of customers worldwide.
Which Lattice Solution Should I Use for Embedded Vision?
Lattice CrossLink™-NX is the industry’s leading low power FPGA for embedded vision processing and sensor interfacing. Built on the Lattice Nexus platform using 28 nm FD-SOI technology, CrossLink-NX delivers up to 75% lower power than comparable FPGAs, packages as small as 4 mm x 4 mm, hardened MIPI D-PHY transceivers at 2.5 Gbps per lane, 5 Gbps PCIe and USB 3.2 support, instant-on configuration (I/O in 3 ms, full device in as fast as 8 ms), and a 100X lower soft error rate than competing FPGAs. Lattice CrossLink-NX is ideally suited for multi-camera sensor aggregation, ISP offload, sensor bridging, and edge AI inferencing. AEC-Q100 qualified automotive-grade options are available.
Lattice CrossLinkU™-NX extends the Lattice CrossLink-NX FPGA family with the industry’s first integrated USB interface in a small embedded vision FPGA, enabling plug-and-play USB camera connectivity via the USB Video Class (UVC) standard, with no custom drivers required.
Lattice Avant™ addresses higher-performance vision processing, including a complete real-time 4K30 embedded vision pipeline from sensor input through FPGA-based ISP to low-latency HDMI output.
The Lattice sensAI™ solution stack enables on-device AI and machine learning inferencing with neural network accelerator IP, Lattice sensAI Studio for training and deployment, pre-trained models for object detection, presence detection, face detection, and defect detection, and RISC-V® integrated reference designs, all at power consumption as low as 1 mW.
The Lattice mVision™ solution stack is an integrated toolkit with reference designs, software tools (Lattice Radiant®and Lattice Propel™), IP cores for MIPI D-PHY, USB3/GigE Vision and ISP pipelines, and hardware development platforms, designed to accelerate time-to-market for embedded vision designs.
For hands-on prototyping, the Embedded Vision Development Kit is a modular Video Interface Platform (VIP) combining Lattice CrossLink and Lattice CrossLink-NX sensor input boards with dual and quad Sony MIPI CSI-2 camera sensors, an ECP5 FPGA processor board, and HDMI output, enabling rapid prototyping and validating for machine vision, automotive camera, drone, smart surveillance, medical imaging, and AR/VR applications.
Embedded Vision and AI
Modern embedded vision increasingly incorporates neural network inference at the edge, enabled by frameworks such as TensorFlow Lite™, ONNX Runtime, and OpenVINO™, alongside dedicated AI accelerators. This allows sophisticated tasks that once required datacenter hardware to run on compact, low power devices.
Embedded vision is no longer an emerging capability. It is the foundation of how intelligent machines perceive and act, and as AI moves decisively to the edge, the companies that win will be those that deliver vision processing with the lowest power, the smallest footprint, and the flexibility to evolve.
For more information, download the Lattice Product Selector Guide or explore Lattice Insights, the official Lattice training academy.