.webp){kind=small}
Technology Solutions
Physical AI
Enable intelligent machines with deterministic sensor processing, real-time control, and low-latency connectivity powered by Altera® FPGAs.
From Fixed Automation to Physical AI
Physical AI is transforming machines from fixed, pre-programmed systems into intelligent platforms that sense, decide, and act in real time. From robotics and autonomous vehicles to industrial automation, these systems must process and synchronize data from cameras, LiDAR, radar, and other sensors with predictable latency.
Altera FPGAs provide a deterministic infrastructure between sensors and AI compute, enabling real-time data aggregation, synchronization, preprocessing, and connectivity. By delivering structured, AI-ready information to CPUs, GPUs, and NPUs, FPGAs help intelligent machines respond with precision, efficiency, and reliability.
The Challenges, and Why FPGA
The Challenge
Unpredictable Real-Time Performance
Autonomous machines require tightly synchronized sensor inputs and precise control responses, where processing delays and timing variations can impact performance.
Why FPGA
Deterministic Real-Time Control
FPGAs process parallel sensor streams with microsecond-level, predictable latency, enabling precise timing and responsive sense-decide-act control loops.
The Challenge
Complex Sensor Connectivity
Physical AI systems integrate cameras, LiDAR, radar, encoders, and industrial interfaces with different data rates, protocols, and timing requirements.
Why FPGA
Adaptive Connectivity and Sensor Integration
Reconfigurable FPGA fabric connects, synchronizes, and preprocesses heterogeneous sensor data, delivering structured information to AI compute platforms.
The Challenge
Safety and Power Constraints
Intelligent machines must operate reliably within strict safety, power, and thermal limits, especially in always-on and human-interactive environments.
Why FPGA
Functional Safety and Energy Efficiency
FPGAs support safety-oriented architectures, fault monitoring, and power-efficient processing for reliable operation across industrial and edge systems.
The Challenge
Increasing Edge AI Demands
Growing sensor workloads require efficient data processing and continuous adaptation to new AI models, protocols, and system requirements.
Why FPGA
In-Fabric Pre-Processing and Flexibility
FPGA-based acceleration filters and structures sensor data before AI compute, while field reprogrammability supports evolving features and long product lifecycles.
Why Altera
Pure-Play FPGA Focus
As a dedicated FPGA company, Altera focuses its technology roadmap, development tools, and support on advancing programmable logic for intelligent and connected systems.
One Portfolio, One Design Flow
The Agilex FPGA portfolio scales from low-power edge devices to high-performance processing platforms, supported by Quartus Prime development software.
Built for Reliable Machines
Access functional-safety-ready FPGA solutions, security capabilities, and reconfigurable architectures that support long-term reliability and evolving system requirements.
Proven Physical AI Ecosystem
Validated partner solutions, development boards, and reference designs—including Holoscan Sensor Bridge—help accelerate integration from prototype to deployment.
Physical AI Applications
From intelligent robotics and industrial automation to autonomous vehicles and smart devices, Altera FPGAs enable deterministic sensor processing, flexible connectivity, and real-time intelligence across Physical AI applications.
Humanoids and Mobile Robots
Synchronize vision, LiDAR, motion, and tactile sensor data to enable real-time perception, navigation, and responsive robotic control.
Factory Automation and Digital Twins
Connect industrial sensors, PLCs, and machine vision systems for deterministic control, real-time monitoring, and digital twin applications.
Adaptive Trajectory Generation
Combine motion feedback, machine vision, and sensor inputs for precise, low-latency robotic movement and adaptive manufacturing.
Medical Imaging and Clinical Systems
Accelerate medical imaging data processing and real-time visualization to support intelligent diagnostic and clinical systems.
Autonomous Drones and Missions
Synchronize camera, LiDAR, radar, and flight sensor data for responsive onboard perception, navigation, and autonomous operations.
ADAS and Software-Defined Vehicles
Process and synchronize camera, radar, LiDAR, and vehicle sensor data to support advanced driver assistance and intelligent mobility.
ST 2110 / IPMX and LED Walls
Enable synchronized, low-latency video transport and processing for IP-based production and high-resolution LED displays.
Automated Test and Instrumentation
Accelerate high-bandwidth data acquisition and deterministic processing for real-time testing, analytics, and instrumentation.
Smart Devices and Vision
Enable responsive, power-efficient smart devices with real-time image enhancement, sensor processing, and structured data for edge AI.
Connecting Sensors to Intelligent Compute
Multi-Sensor Ingest
Connect cameras, LiDAR, radar, and other sensors through scalable interfaces with deterministic data aggregation.
MIPI CSI-2 · SLVS-EC · GMSL / FPD-Link
Precision Timing and Control
Maintain synchronized sensor inputs and predictable timing across motion control, navigation, and industrial systems.
IEEE 1588 / PTP · TSN · IMU synchronization
Data-Path Acceleration
Offload sensor preprocessing, compression, and data movement to deliver structured information efficiently to AI compute.
Sensor fusion assist · PCIe DMA · Protocol bridging
Security and Platform Awareness
Support secure, reliable machine operation through sensor monitoring, fault detection, and hardware-assisted protection.
Fault detection · Inline encryption · Secure boot assistance
Get Started
Common Questions
The FPGA data plane captures, timestamps, transforms and transfers sensor data while implementing timing-sensitive interfaces and control logic. CPUs and GPUs can run planning or inference while programmable logic handles the validated real-time data path.
Split workloads when high-rate interfaces and bounded control must coexist with large AI models. Put supported preprocessing and timing-sensitive logic in the FPGA, and place model execution on the GPU when its software ecosystem and compute capacity fit.
Define a common time base, timestamp near acquisition and account for interface and pipeline delays. Track clock accuracy and timestamp provenance so downstream fusion can distinguish capture time from arrival time.
Use streaming buffers when each stage can process data incrementally and the algorithm does not require a complete frame. Full-frame storage is necessary for some temporal or global operations and adds memory traffic and acquisition delay.
Detect stale timestamps, missing samples and buffer overflows before applying data to control. The system design must define degraded operation or a safe response independently of whether AI inference continues to run.