In the rush to deploy large models, the industry often overlooks the "Energy Tax" of inference. At Convert Edge, we are currently deep into Project 1: The Architecture of an Energy-Aware Neuro-Symbolic Inference Engine via Hardware Telemetry.
Working alongside Eleanor Korobok, we are bridging two worlds: the probabilistic power of neural networks and the deterministic rigor of symbolic logic.
The Technical Challenge
The goal is to create an inference engine that doesn't just "predict," but "reasons"—all while monitoring its own power consumption via hardware telemetry. By feeding real-time CPU/GPU thermal and power data directly into the inference loop, the engine dynamically adjusts its symbolic logic complexity to maintain a specific energy budget.
Why Neuro-Symbolic?
Pure neural approaches are energy-hungry black boxes. By integrating symbolic logic, we allow the engine to "short-circuit" compute-heavy operations when the logic determines a simpler path is sufficient.
Through our collaboration with Eleanor, we are utilizing low-level hardware interfaces to capture telemetry, allowing the software to make energy-aware decisions at the sub-millisecond level. This is the future of edge AI: intelligence that understands its own environmental cost.
We are building the next generation of efficient, logic-driven AI. convertedge.ca