Capability

SWaP-optimized edge AI: only as much compute as each input needs.

A heterogeneous edge-AI architecture that routes each input to the cheapest specialist that can handle it, escalating to more expensive processing only when a confidence check says it is warranted.

Discuss an Edge AI Evaluation Mission Systems

Problem

Always-on, full-power inference wastes compute most edge platforms cannot spare.

Size, weight, and power constraints force a hard tradeoff: run every input through the most capable model available and pay for it in energy and latency, or run something cheaper and lose accuracy on the inputs that actually needed the expensive path. RTG built and measured an architecture that avoids that tradeoff for a large share of inputs by deciding, per input, how much processing is warranted.

Architecture Pattern

Heterogeneous specialists: compact, temporal, and contextual model paths
A confidence-based arbiter decides per input whether to stop early or escalate
Escalation reuses every specialist already evaluated instead of discarding compute
An optional working-memory mode trades additional compute for higher accuracy when the mission calls for it

Measured edge result

Real networks, real edge-hardware measurement, careful interpretation.

Accuracy vs. Compute

87.7% accuracy at an average of 1.50 experts consulted per input, versus 88.2% for a baseline that always runs the full heterogeneous set at 3.00 experts per input.

Energy

Approximately 59.5% less energy at a 15W power mode and 58.5% less at a 10W power mode versus the always-on baseline in the measured edge-hardware test setup.

Latency and Power Budget

The useful result is a practical accuracy-for-compute tradeoff: most of the always-on accuracy at roughly half the specialist evaluations, with measured energy savings under constrained power modes.

Claim boundary

Strong evidence, careful claims.

This is R&D-stage evaluation on a representative task, not a fielded product claim. A homogeneous baseline was more accurate outright than the heterogeneous adaptive design in absolute terms. The demonstrated advantage is the accuracy-for-compute tradeoff at a fixed edge budget, not an unconditional accuracy win. The result should be carried forward into mission-representative sensor tasks before being treated as deployment evidence.

What this proves

Confidence-based escalation can recover most of an always-on system's accuracy at roughly half the specialist evaluations
The measured energy savings are material across two constrained power modes
The architecture is portable as a pattern across edge-AI hardware targets
A working-memory-augmented mode gives an accuracy and energy dial for critical inputs

Where this fits

Edge AI belongs beside sensing, telemetry, and operator workflows.

The same RTG Mission Systems pattern applies here: constrain the edge workload, preserve evidence, keep the decision logic reviewable, and connect results into operator or support workflows. This makes SWaP-optimized inference a natural companion to embedded Linux, sensor fusion, PASSIVE-SHIELD prototype work, and broader mission-system integration.