Perception to Actuation:
Real-Time Robotics Compute
We build deterministic compute and sensor-processing pipelines that bring autonomy to robots, unmanned systems, industrial vehicles, and next-generation automated machines.
High-speed sensor ingestion (camera, IMU, LiDAR, industrial I/O)
FPGA-accelerated perception & ML inference
Real-time fusion, control loops, and safety logic
Deployable platforms for robotics controllers and autonomous systems
Automation Teams Struggle With Real-Time Complexity
Exploding Verification Load
As SoCs integrate PCIe/CXL, SerDes, JESD, Ethernet, and accelerators, verification effort scales far faster than design complexity.
High-Speed Interfaces Are Failure Points
PAM-4 signaling, lane alignment, jitter tolerance, protocol timing, and LTSSM behavior demand specialized, protocol-level expertise.
FPGA Prototype Bring-Up Is Slow
IP integration, platform setup, software enablement, and link bring-up often take weeks without proven reference platforms.
Post-Silicon Debug Is Unpredictable
Limited visibility and a lack of robust VIP, stress tools, and analyzers push critical issues late into the schedule.
Platform Architectures for Autonomous Machines
Kritin VPX SBC Family (Flagship Robotics Controller)
- High-performance FPGA + ARM control
- Multi-camera ingest + IMU + encoder support
- Deterministic control-loop execution
- Suitable for mobile robots, UAVs, ground robots, and AMRs
Avant G70 PCIe Mini Board
- Compact ML inference accelerator
- Ideal for robotic arms, industrial gantries, small AMRs
- PCIe-connected edge AI coprocessor
Aquila DAQ Platform
- Multi-sensor DAQ for perception workloads
- Camera + ADC ingest with FPGA preprocessing
- Used in robot inspection and navigation subsystems
ARINC 818 Displays & Video Chain (A-3.1.2)
- High-speed, low-latency visual pipelines
- Rugged displays for robotics HMIs
- Multi-format video processing for teleoperation
Platform Architectures for Autonomous Machines
Logic Fruit delivered:
- FPGA-based multi-camera ingest (Aquila)
- Real-time IMU + encoder fusion (Kritin SBC)
- ML inference mapped to FPGA fabric
- Sub-10 ms perception → actuation loop
- Safety-certified control path drivers
- Full system-level validation using our verification methodology
Outcome Highlights
- 40% lower end-to-end latency
- Deterministic path planning even under load
- Reduced compute power consumption via FPGA acceleration
- Faster field deployment using COTS platforms