SYSTEMS
When physics
sets the stack
Infrastructure for physical-world AI: closed loops, finite power, event-native sensors and programs that require evidence. These are the domains where spiking networks and neuromorphic silicon stop being research and start being engineering.
ROBOTICS
Sub-millisecond sensor-to-actuator loops.
The constraint: Frame-based inference adds latency you cannot schedule around.
Why spikes: Timestep-native computation. Event-driven activation - the network is quiet when the arm is still. On-device inference without cloud round-trips.
Typical workloads: Tactile reflex, motor coordination, grasp planning on sparse contact events, always-on manipulation monitoring.
What thrindex provides: PyTorch authoring → compile → deterministic simulator for validation before hardware → sealed artifact for deployment.
DRONES & UAVS
Every gram of battery is flight time.
The constraint: Onboard inference must fit a watt budget, not a datacenter budget.
Why spikes: Energy scales with information rate.A hovering drone with a quiet scene should not burn GPU-class power on static frames.Typical workloads: Obstacle detection on event cameras, acoustic keyword spotting, vibration anomaly on airframe, low-power gating before waking a heavier model.
What thrindex provides: Modeled energy per inference in CI. Templates for event-native and audio pipelines. Same artifact from bench to airframe.
AEROSPACE & SPACE
Finite power for years.
No cloud. Verifiable before launch.
The constraint:
No field service. Software must be verifiable before launch.Why spikes: µW-class always-on sensing. Deterministic execution with seeded, reproducible output. Frozen artifacts that can be re-verified offline, years later.
Typical workloads: Payload health monitoring, star tracker gating, structural vibration, onboard sensor fusion at the edge of connectivity.
What thrindex provides:
.thxartifact as deployment contract. Compile once, verify forever. Traceability-oriented engineering from day one.
DEFENSE
Reproducible builds, offline verification, evidence auditors can review.
The constraint: High-assurance programs require reproducible builds, offline verification, enclave deployment, and evidence packages auditors can review - not a Jupyter notebook on a laptop.
Why spikes: Edge autonomy without connectivity. Low SWaP (size, weight, and power). Adaptation on-chip without cloud dependency (roadmap).
Typical workloads: Signals intelligence edge processing, unattended perimeter sensing, EW-adjacent pattern detection, autonomous platform perception at the tactical edge.
What thrindex provides:
Self-hosted control plane(roadmap). No telemetry phone-home by default. Open SDK stays generic, program-specific work segregated.
