Performance and Reliability
Lowest latency and power consumption. Highest FPS/W, TPS/W, precision. Full multimodal, sensor fusion enablement. Operates reliably from -40°C – 85°C.
SiMa.ai enables a new class of intelligent drones with real-time Physical AI — integrating vision, sensor fusion, and multimodal AI into a unified architecture for full end-to-end drone applications.
Customer and Partner Stories
Delivering real-time in-flight intelligence under 10W, the industrial-grade platform optimizes SWaP constraints, ensuring maximum flight time and efficiency for mission-critical edge deployments.
Lowest latency and power consumption. Highest FPS/W, TPS/W, precision. Full multimodal, sensor fusion enablement. Operates reliably from -40°C – 85°C.
Easy-to-use design tools with agentic AI framework, Palette Neat. Partner solutions. Chip-Down Design (CDD) support or SoM.
Delivering Size, Weight and Power (SWaP) requirements on a single chip. 10 year lifetime. No cloud latency, cost, or privacy/security concerns.
Customers choose levels of accuracy and resolution. Scales across model families without breaking power budget.
The purpose-built Modalix MLSoC integrates seamlessly across every layer of the drone stack — from hardware to high-level applications. We offer multiple form factors and chip-down design support to best address customer needs.
Comprehensive drone stack reduces complexity and integration risk. Support from perception to control on a single platform eliminates fragmented systems to improve reliability.
Industry Leading Performance
Stereo Depth Calculation (LightStereo)
Feature Point Detector (SuperPoint)
Feature Matcher (SuperGlue)
Loop Closure (NetVlad)
Obstacle Awareness (OamNet)
Obstacle Detection / Segmentation (YoloV8)
Real-world capabilities powered by the Modalix MLSoC.
How It Works
LLMs used to convert voice or text prompts into real-time image analysis commands on the edge.
Key Benefits
Read the definitive guide on how SiMa.ai's Modalix MLSoC is powering the next generation of autonomous drones.