Aipowertoken

Comprehensive analysis of AI-based crypto tokens, examining their technical architectures, limitations, and future prospects in decentralized AI ecosystems.

Research on E-PoW consensus that integrates AI matrix computations into blockchain mining to salvage computing power in 6G networks.

Analysis of Evolved-Proof-of-Work consensus integrating AI training with blockchain mining to salvage computing power in 6G networks.

Systematic evaluation of AI energy estimation tools comparing CodeCarbon and ML Emissions Calculator against ground-truth measurements across hundreds of AI experiments.

Analysis of AI-focused HPC data centers providing grid flexibility at lower cost compared to general-purpose HPC data centers, using real-world computing traces and cost models.

A study introducing the Analytical-Reliability Benchmark (ARB) to evaluate reasoning integrity in large language models applied to energy-system analysis, with results from GPT-4/5, Claude 4.5, Gemini 2.5, and Llama 3.

ECO2AI is an open-source tool for tracking energy consumption and CO2 emissions of ML models, promoting sustainable AI development through accurate regional emissions accounting.

Research on EconAgentic, a Large Language Model framework for simulating and optimizing DePIN markets using AI agents, token economics, and smart contracts.

Analysis of energy consumption and carbon emissions testing challenges for AI-driven IoT services, including technical approaches, experimental results, and future directions.

Research on energy consumption trade-offs in HPC-scale Deep Learning, featuring Benchmark-Tracker tool for measuring computing speed and energy efficiency of AI algorithms.

Research on how ITS and AI technologies improve energy conservation and reduce emissions in transportation systems, focusing on sensors, networking, and AI services.

A resilient binarized neural network with 32,768 memristors powered by miniature solar cells, enabling self-powered edge AI with digital near-memory computing architecture.

VCT is an unsupervised transformer-based framework that tokenizes images into disentangled visual concepts, achieving state-of-the-art results in representation learning and scene decomposition.