What question did this study set out to answer?

This research aims to develop a formal framework to reduce energy consumption in AI computing through lightweight prompt filtering.

April 10, 2026Open Access

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

Puntos clave

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.

Resumen

v2.2 — Major Update: Robustness & Security Milestone. Introduction of the M0 Decontamination Membrane, achieving zero residual noise across 10 stress-test cases (100% pure noise, malicious injections, and high-entropy alphanumeric interference). Stress-tested on 1,000,000 samples with a record throughput of 9,036 req/sec on consumer hardware (HP ProBook 640 G6). Synaptic memory expanded to 443 trained synapses with a new 5-phase training protocol. Formalized the Decontamination Theorem (δ=0.5) and verified a 72% average energy gain with <1ms latency.

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Cite This Study

Anselme Benedict Sawadogo (Wed,) studied this question.

synapsesocial.com/papers/69d895be6c1944d70ce06cb8 https://doi.org/https://doi.org/10.5281/zenodo.19475957

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