The Center for Responsible Machine Learning and the Institute for Energy Efficiency at UC Santa Barbara are advancing the state of the art in artificial intelligence, machine learning, natural language processing, and computer vision while comprehending important societal impacts of AI, particularly driving improved energy efficiency in both AI training and inference.
The first step to improving energy efficiency in AI is to benchmark current state-of-the-art AI models. To tackle such challenges, Prof. William Wang from CRML and his students proposed the HULK platform for energy efficiency benchmarking in natural language processing research. A typical NLP pipeline includes pretraining, fine-tuning and inference phases. The platform compares the end-to-end training and inference efficiency from the perspectives of time and budget. Different from previous leaderboards, in order to compare the general training and inference efficiency of pretrained models on different tasks, the platform offers a combined score on 3 different classic NLP tasks including natural language inference, sentiment analysis and named entity recognition. The HULK platforms offers a practical reference for efficient model and hardware selection, especially for enterprise training their own AI models with private data.
AI is becoming widely available on all manner of devices. AI applications on such devices are demanding more and more computing resources and data storage. Therefore, slight improvements in AI efficiency could make a huge impact as these solutions scale worldwide. Green AI research in IEE and CRML will contribute to much more energy efficient and practical AI solutions on millions of devices worldwide.