E-ZERO Protocol: A Formal Framework for AI Compute Energy Reduction via Lightweight Prompt Filtering

Key Points

• This research aims to develop a formal framework to reduce energy consumption in AI computing through lightweight prompt filtering.
• Introduced the E-Zero Protocol with prompt filtering techniques.
• Implemented stress-testing on 1,000,000 samples to evaluate performance.
• Expanded synaptic memory capacity to 443 trained synapses.
• Verified energy savings using the Decontamination Theorem.
• Achieved zero residual noise across various stress-test scenarios.
• Recorded a throughput of 9,036 requests per second on standard consumer hardware.
• Demonstrated a 72% average energy gain with less than 1 millisecond latency.

Abstract