energy-efficient-ai

sparse ternary AI stack enabling efficient frontier intelligence without hyperscaler-scale infrastructure.

Measure and audit intelligence gained per joule spent. Proof-of-Learning prototype.

Open-source glaucoma detection AI for mobile/low-resource clinics using synthetic training data

Proof-of-Contribution Chain: traceable contributions in a Web3-adjacent ecosystem (without classic blockchain complexity) earn voting power on roadmap priorities. Closed Gem Economy: gems act as currency in the NexRealm Marketplace for assets, earned through real contributions. Cross-Project Gems: gems become portable across creative ecosystems.

Ensemble Deep Random Vector Functional Link with Skip Connections (edRVFL-SC) No GPU required • 100× faster training

Relational Time Engine (RTE): runtime density regulation for compute-efficient transformer inference. Demonstrates up to 75% layer reduction with improved latency and throughput.

This repo contains only the specific mechanism (fixed-K speculative decoding) that's fully validated and reproducible as claimed. See "Relationship to SDSIE" below for more.

A minimal protocol for managing AI replica clusters as a breathing data center: staging, filtering, compressing, auditing, discarding, and promoting only valuable outputs to a core data center.

42 — adaptive-depth inference: match accuracy, cut compute and energy 20-83%.

Reliability-constrained visual-token budgeting for energy-efficient vision-language model inference.

42 — adaptive-depth inference

Joule Wars: the AI race for energy efficiency — who produces the most useful intelligence per joule. Concept by Michał Piszczek.

GAP is a biologically plausible learning algorithm designed for Dynamically Gated Analog Crossbars (DGAC). It bridges the gap between the energy efficiency of local Hebbian learning and the global optimization power of backpropagation by utilizing dynamic Riemannian curvature.

A lightweight protocol for coordinating central carrier models and specialized wing models to reduce unnecessary large-model activation through traceable inference relay.

The Enterprise Standard for Energy-Aware AI. Shatter the 2026 AI Memory Wall with a high-performance orchestration layer in a secure and constrained environments.

An experimental research project investigating quantum-inspired optimization for energy-efficient recommender systems. The project studies whether a Quadratic Unconstrained Binary Optimization (QUBO) formulation combined with simulated annealing can optimize recommendation selection while maintaining recommendation quality.

An energy-efficient neuron activation system for AI models. Concept by Baris (2025).

Sub-Watt Saccadic Vision & Power-Aware KV-Cache Engine for Hyperscale Agent Data Centers (70%+ Energy Reduction).

Zero-Carbon AI Architecture powered by Information-Entanglement Stabilization Algorithm (IESA)