Built for every learning and engineering level
The interface explains the same system at different levels of depth. A high school student can understand signals and ports. A PhD researcher can inspect compute resources, buses, lanes, power relationships, and bandwidth constraints.
Beginners & High School Students
Learn what pins, buses, ports, sensors, and cameras do through clean visual maps and guided software views.
University & PhD Researchers
Model system architecture, compute partitioning, imaging pipelines, memory flow, and edge AI resource allocation.
Professional Engineers
Plan integration around interfaces, cameras, buses, PCIe, Ethernet, power rails, and software deployment targets.
Scientists & Robotics Teams
Bridge experiments, autonomous systems, sensing, actuation, and AI inference using a visual topology model.
A.I.-driven system resource topology visualizer
Your Pi dashboard maps the standard peripheral interfaces. On Jetson Thor, the view expands into a full platform-level resource map. A.I.-assisted interpretation helps users understand connections, bottlenecks, compute planning, and deployment choices more clearly.
Raspberry Pi view
A clean dashboard for teaching and deploying real embedded projects with the exact interfaces makers and professionals use most.
GPIO
Visualize digital I/O relationships for sensors, relays, LEDs, motors, and quick hardware experiments.
SPI
Track high-speed peripheral connections for ADCs, displays, storage, and control devices.
I2C
Map shared two-wire buses for sensors, RTCs, IO expanders, and address-based device layouts.
UART
Organize serial communications for modems, microcontrollers, debugging ports, and telemetry.
USB
Identify host peripherals like cameras, storage, AI accelerators, and wireless adapters.
HDMI
Plan display output for education dashboards, kiosks, robotics control, and edge monitoring.
PCIe
Show expansion paths for higher-bandwidth devices and future performance-focused Pi workflows.
Jetson Thor view
Go beyond interface mapping into compute, imaging, data movement, and platform resource orchestration.
Interfaces
Platform-wide visibility into PCIe lanes, NVLink, NVCSI camera lanes, Ethernet MACs, CAN buses, I2C control buses, SPI sensors, GPIO banks, and power rails.
Compute Resources
Inspect GPU SM partitions, CPU cores, DLA units, and the relationship between workloads and available acceleration hardware.
Imaging & Vision
Show ISP pipelines, camera ingress, and sensor paths for robotics, autonomy, machine vision, and research capture systems.
Bandwidth
Understand memory bandwidth pressure, interface bottlenecks, data flow, and how throughput limits system behavior.
Control
Coordinate buses and control planes for motion systems, robotics, instrumentation, and embedded management.
Power Awareness
Relate power rails and compute blocks to practical deployment choices in mobile, edge, and industrial platforms.
What the A.I. software helps users do
Teach visually with A.I.
Make hardware and system architecture easier to understand in classrooms, labs, and demonstrations with A.I.-supported explanations.
Prototype faster
Connect ports, buses, cameras, and compute units with less trial and error during early builds using A.I.-guided system insight.
Deploy better systems
Plan around resource limits before writing integration code, wiring sensors, or scaling AI workloads with A.I.-assisted platform awareness.
Bridge disciplines
Create a common A.I.-enhanced view for software developers, scientists, robotics engineers, and embedded hardware teams.
How A.I. Helps
The platform integrates artificial intelligence to analyze hardware resources, interfaces, and compute systems. It is already being used in advanced engineering environments including aerospace, robotics, transportation, drone systems, and research programs in government, universities, and industry.
Engineering Productivity Impact
During internal testing on multi‑sensor drone systems, the platform enabled scientists and engineers to complete analysis and system planning work that previously required roughly three months of engineering effort in a single day. The largest gains occurred in multi‑sensor integration, compute pipeline planning, and system topology analysis.
Measured Result
3 months → 1 day
Multi‑sensor drone system analysis and planning acceleration.
Detect Bottlenecks
A.I. analyzes data flow between interfaces, buses, compute units, and memory bandwidth to help identify system bottlenecks before deployment.
Sensor Fusion Planning
Plan multi‑sensor systems combining cameras, radar, LiDAR, and environmental sensors while visualizing compute and interface constraints.
Accelerate Learning
Students and researchers can quickly understand hardware relationships between GPIO, buses, cameras, and compute blocks using A.I. explanations.
Mission‑Grade Systems
Used in advanced programs for aerospace, robotics, transportation systems, and drones including projects involving government, universities, and industry.
Reference System Architecture
The platform helps engineers visualize and design complex multi‑sensor systems that combine FPGA timing, AI compute platforms, and real‑time control systems. A typical architecture used in advanced robotics, aerospace, and drone platforms is shown below.
Sensors
Cameras
LiDAR
Radar
Environmental Sensors
FPGA Timing Layer
Xilinx UltraScale+ FPGA
Nanosecond Precision
Sensor Synchronization
AI Compute
Dual NVIDIA Jetson Thor
Sensor Fusion
AI Inference
Control Systems
Flight Control
Laser-Enhanced Propulsion
Navigation Logic
Nanosecond Timing
The FPGA layer provides deterministic nanosecond‑level timing for synchronizing sensors and propulsion control loops.
AI Sensor Fusion
Jetson Thor platforms process multi‑sensor inputs and run AI fusion pipelines to produce real‑time system awareness.
Trusted in Advanced Programs
The platform is used in advanced engineering environments where complex sensor systems, high‑performance computing, and real‑time control must work together. Typical deployments combine edge AI platforms, FPGA acceleration, and multi‑sensor fusion systems.
Aerospace Systems
Used in experimental aerospace platforms requiring precise control systems, advanced sensing, and high‑speed compute coordination.
Autonomous Robotics
Supports robotics architectures combining cameras, LiDAR, radar, and embedded AI processors.
Transportation Systems
Helps engineers model compute and sensor relationships for next‑generation transportation and mobility systems.
Drone / UAV Development
Used in multi‑sensor drone architectures combining edge AI systems with FPGA timing precision.
Example Reference Architecture
Current deployments include systems combining Xilinx UltraScale+ FPGA hardware for nanosecond‑precision timing, Raspberry Pi 5 control systems, and NVIDIA Jetson Thor platforms for high‑performance sensor fusion and AI workloads. These hybrid architectures enable precise coordination of sensors, propulsion systems, and real‑time control loops.
Typical Stack
Xilinx UltraScale+ FPGA
Raspberry Pi 5
Dual NVIDIA Jetson Thor
Multi‑sensor fusion
Product listings
Separate software packages keep Raspberry Pi and NVIDIA Jetson projects organized by platform family and deployment needs.
Raspberry Pi A.I. Software
Optimized for GPIO-centered embedded computing, physical computing education, and edge prototyping, with A.I.-supported topology insight and interface understanding.
Raspberry Pi 5 Software License
Full topology dashboard, interface mapping, project templates, and modern Pi workflow support.
Raspberry Pi 4 Software License
For legacy deployments, education labs, and established prototypes.
Older Raspberry Pi Models
Support package for Pi 3, Zero family, and older classroom or field systems.
NVIDIA Jetson A.I. Software
Built for edge AI, computer vision, robotics, advanced imaging, and high-resource platform planning, with A.I.-driven topology analysis.
Jetson Thor Software License
Complete system resource topology visualizer with compute, interface, and bandwidth modeling.
Jetson Orin / Xavier Package
For current robotics and embedded AI fleets using earlier Jetson generations.
Older Jetson Models
Support option for Nano, TX families, and legacy development environments.
1. Choose platform family
Select Raspberry Pi or NVIDIA Jetson based on your project hardware.
2. Match your model generation
Choose Pi 5, older Pi, Jetson Thor, or earlier Jetson platforms.
3. Purchase software access
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Payment page
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Pre-Order Pricing Notice
70% pre-order discount available through October 1, 2026.
Starting October 1, 2026, pricing changes to regular pre-launch pricing.
Official release date: December 1, 2026.
Pre‑order discount countdown: calculating…
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Use your business PayPal account button which supports both PayPal accounts and direct credit card payments. You can also integrate the PayPal JavaScript SDK so customers can pay with PayPal, Visa, Mastercard, American Express, or other supported cards.
Bank-to-bank payment request
For institutions, labs, universities, and enterprise buyers that require invoicing or direct transfer workflows.
Technology Partners
TopologySuite is used by leading engineering and robotics developers building advanced systems for AI, robotics, aerospace, and autonomous platforms.
Fornux
Fornux, developers of the C++ Superset, are a technology partner using TopologySuite in their advanced robotics and AI systems.
Fornux currently deploys TopologySuite on NVIDIA Jetson Orin platforms and is preparing support for the upcoming NVIDIA Jetson Thor systems.
TopologySuite assists their engineers in visualizing system topology, sensor fusion pipelines, FPGA timing relationships, and GPU compute resource allocation across embedded AI platforms.
Visit Fornux