Ecosystem Explorer
Where Unleashed Intelligence meets physical constraints across 5 Natural Domains.
AI cannot scale if it remains chained to remote datacenters or constrained by the power grid. True edge computing must operate within the strict physical boundaries of the environments it serves. By eliminating the memory wall, Sibacus enables high-performance inference at milliwatt budgets across five key natural domains.
Every domain below runs the same pattern: ship one uniform base model, then let each device individuate through on-device fine-tuning to its own environment. The result is an Army of Experts—uniform in domain, individual in experience. It is the edge-native counterpart to the cloud's Mixture of Experts: where MoE routes logically inside one model, an Army of Experts distributes physically across a fleet, each specialist persisting unpowered in non-volatile RRAM until its moment.
Space: Sovereign Orbital Compute
LEO satellite constellations operating on solar budgets.
Low Earth Orbit (LEO) satellites operate in a physical vacuum where convective cooling is impossible. A standard 400W GPU cannot dissipate heat and exceeds the shared solar power budget of the entire satellite bus.
The Sibacus Win: BSA-CIM runs model inference at ~1W. It is passively cooled, natively immune to radiation-induced bit flips, and enables real-time orbital intelligence for maritime surveillance and environmental tracking.
Sky: Autonomous Aerial Intelligence
Extending UAV payload and battery flight times.
In drones and unmanned aerial vehicles, every gram of payload and milliwatt of compute energy directly reduces flight endurance. Standard GPU boards consume dozens of watts, generating heat that requires bulky heatsinks and fans.
The Sibacus Win: Moving compute inside RRAM allows continuous real-time vision pipelines (like crack detection on power lines or canopy anomalies) to run on a fraction of a watt. This eliminates cooling fans and preserves the battery for motor propulsion.
Seas: Autonomous Maritime Intelligence
Ocean navigation, shipping corridors, and marine conservation.
Open ocean cargo corridors and remote marine buoys operate completely disconnected from terrestrial networks. Salty, corrosive maritime air degrades active cooling fans and vents in weeks, requiring sealed, passively cooled hardware.
The Sibacus Win: Completely sealed, fanless RRAM compute blocks run weather routing, collision avoidance, and acoustic tracking models on-vessel or on-buoy. They operate at milliwatt budgets, surviving on local solar harvesting.
Streets: Local Learning & Mobility
Powering smart cities, clients, and vehicles without network tethering.
Client workstations, autonomous delivery fleets, and smart city nodes need to process high-frequency vision and audio. Relying on constant cellular connections introduces latency, dropouts, and telemetry privacy risks.
The Sibacus Win: Low-power local execution enables client devices (S-Neo) to translate and process LLMs natively. More importantly, local learning allows models to adjust weights directly on-device using local feedback loops, adapting to local lighting, terminology, and conditions.
An "Army of Experts" — Assisted Driving
Where the cloud runs a Mixture of Experts inside one model, a fleet fields an Army of Experts across the road.
Every vehicle ships the same certified base expert, then individuates through its own lived experience—uniform in domain, individual in experience. The framing nests across two tiers, both underwritten by non-volatile on-device RRAM.
One platform hosts many complete specialists—lane geometry, pedestrians, signs, cross-traffic, near-field parking. The "router" is driving context: highway cruising wakes lane-keep and vehicle-tracking while pedestrian and parking experts sit dormant at zero power. Spatial sparsity inside a sealed, passively-cooled ECU.
Each expert starts uniform, then locally fine-tunes to its own roads, climate, regional signage, and sensor aging via restricted-update RRAM writes. Same DNA, different life—every car becomes the expert of its own beat. A capability a GPU fleet can't cheaply carry: divergent per-vehicle weights that persist unpowered and update in place.
The safety-critical perception core stays certified and frozen; local fine-tuning rides only on the adaptation tier. "Individual in experience" a regulator can accept under ISO 26262—not silent on-vehicle weight drift.
Local gains distill back to sharpen the common base, which is re-certified centrally and re-broadcast—each vehicle keeps its personalization while the shared expert compounds across the fleet.
Soil: Decentralized Ecological Tracking
Continuous agricultural and conservation tracking.
Monitoring agricultural health, soil chemistry, and forest degradation requires arrays of remote IoT sensors. In deep jungle canopies or vast crop fields, there is no cellular signal or grid power—sensors must survive on solar harvesting or single coin cells.
The Sibacus Win: Our non-volatile RRAM weight storage offers true zero-standby leakage. Sensors wake up instantly upon detecting an event, run model inference on microwatts, and sleep without wasting power.