4 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites
Lei Jiang, Adrian Ildefonso, Daniel Loveless +1
Large language models (LLMs) impose rapidly growing energy demands, creating an emerging energy and carbon crisis driven by large-scale inference. Solar-powered, AI-enabled low Ear…
QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Zhenxiao Fu, Lei Jiang, Fan Chen
Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardwar…
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences
Zhenxiao Fu, Fan Chen, Shan Zhou +2
Throughout its lifecycle, a large language model (LLM) generates a substantially larger carbon footprint during inference than training. LLM inference requests vary in batch size,…
IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning
Fan Chen, Shahzeen Attari, Gayle Buck +1
To improve privacy and ensure quality-of-service (QoS), deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing, significantly i…