Safety‑critical autonomous platforms (e.g., vehicles, drones, industrial robots) require deterministic, low‑latency perception and decision‑making. Current AI pipelines often rely on cloud off‑loading or heavyweight models that exceed the memory and power budgets of edge compute, leading to latency spikes and reliability concerns. TraceWeave delivers Phase‑TITAN, a post‑transformer neural architecture engineered for constant‑memory operation on constrained edge processors.
Funding
Funding not disclosed
Founders
Product
Problem
Safety‑critical autonomous platforms (e.g., vehicles, drones, industrial robots) require deterministic, low‑latency perception and decision‑making. Current AI pipelines often rely on cloud off‑loading or heavyweight models that exceed the memory and power budgets of edge compute, leading to latency spikes and reliability concerns.
Solution
TraceWeave delivers Phase‑TITAN, a post‑transformer neural architecture engineered for constant‑memory operation on constrained edge processors. The model performs native multi‑modal encoding of lidar, camera, radar, and inertial data, enabling real‑time sensor fusion and collision‑avoidance inference without external compute. Because memory usage does not grow with input length, Phase‑TITAN provides predictable latency across billions of devices and supports over‑the‑air distributed learning updates. The software stack includes a lightweight runtime, hardware‑abstraction APIs, and integration hooks for existing autonomous stacks, allowing OEMs to embed on‑device intelligence directly into safety‑critical control loops.
Target Audience
Primary customers are OEMs and system integrators developing autonomous vehicles, aerial drones, and industrial robotics that require on‑device AI for safety‑critical perception and control.
Features
- Constant‑memory post‑transformer core that guarantees O(1) memory footprint regardless of sequence length
- Native multi‑modal encoder supporting lidar, radar, camera, and IMU streams in a unified representation
- Edge‑native runtime optimized for ARM, RISC‑V, and low‑power ASICs with sub‑10 ms end‑to‑end latency
- Distributed learning framework that pushes model updates to devices via secure OTA without retraining on‑device
- Deterministic inference pipeline with hard real‑time guarantees suitable for ISO 26262 and IEC 61508 compliance
- Hardware‑agnostic SDK (C++/Python) and FFI bindings for ROS2, AUTOSAR, and custom firmware stacks
- Open‑source reference implementation on GitHub with CI‑tested benchmarks and profiling tools
- Built‑in security layer: encrypted model weights, tamper‑detectable runtime, and role‑based access control for OTA updates