Engineering note
FPGA, NPU or MPU? Partitioning an Edge AI Architecture
A practical guide to partitioning edge AI workloads across FPGA, NPU, MPU and MCU resources by latency, power and software needs.
Read the note →Low-latency perception
ChronaVision for Real-Time Decisions
ChronaVision is a configurable event-based vision system combining event-based image sensing, FPGA preprocessing, embedded compute and mission interfaces for low-latency autonomous, defense and industrial perception.
Frame-based cameras are excellent for many imaging tasks, but fast-moving platforms can lose valuable decision time while complete frames are exposed, transferred and processed. An event-based vision system changes the data path: pixels report brightness changes asynchronously, so the compute chain can focus on motion and temporal structure instead of repeatedly moving unchanged background data.
Alvexis combines the sensor interface, FPGA front end, embedded compute and rugged electronics needed to turn that sensing principle into an integrable platform. The architecture is intended for autonomous systems, robotics, industrial motion analysis and research programs that need deterministic, low-latency perception.
System architecture
1
Event sensing
Capture asynchronous scene changes with precise timestamps.
2
FPGA front end
Filter, timestamp, aggregate and prepare event streams deterministically.
3
Embedded compute
Run tracking, fusion and AI workloads on the selected NPU, MPU or MCU tier.
4
Mission interfaces
Publish decisions and metadata over Ethernet, CAN-FD, RS-422 or customer-specific I/O.
Capabilities
Engineering scope
- Event-stream capture and timestamp handling
- FPGA-based filtering and region-of-interest processing
- Optional frame, thermal or inertial sensor fusion
- Local inference without dependence on a remote cloud
- External trigger and synchronization options
- Carrier, enclosure and interface customization
Applications
Event-based or frame-based?
For the engineering trade-offs behind sensor timing, software, SDK design and hybrid perception, read our event-based vision vs frame-based vision guide.
Typical use cases
- Fast target and motion tracking
- Robotic manipulation and inspection
- High-dynamic-range outdoor perception
- Research and algorithm validation
- Autonomous platform payloads
- Low-bandwidth edge analytics
Specification discipline
What is defined during a pilot.
Values depend on the selected sensors, processor, interfaces, environment and acceptance method.
| Area | Definition |
|---|---|
| Sensor-to-decision latency | Defined with the selected sensor, algorithm and interface; sub-millisecond paths are a design objective, not a universal claim. |
| Data reduction | Application dependent; sparse scenes can reduce transferred data substantially compared with continuous full-frame pipelines. |
| Environmental design | Thermal, EMI and ingress requirements are tailored to the deployment envelope. |
| Program status | Platform architecture and modules are under development; final specifications follow pilot validation. |
Related
Continue exploring.
Engineering note
Event-Based vs Frame-Based Vision: Choosing for the Decision Loop
Compare event-based and frame-based vision by latency, data behavior, image content and integration trade-offs for autonomous edge systems.
Read the note →
Perception Systems
ForgePulse
ForgePulse wireless vibration monitoring hardware for predictive maintenance, high-bandwidth condition sensing and edge feature extraction in industrial assets.
Explore system →Define your ChronaVision configuration.
Send the interfaces, environment, decision metric and pilot quantity. We will identify the architecture and validation plan.