99 citations · 115 across the 13 of their papers we have counts for
13 papers
Crypto x AI, AI x Crypto: A Survey
Sarah Allen, Pranay Anchuri, James Austgen +22
The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportuni…
Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
Jared Fernandez, Clara Na, Yonatan Bisk +2
Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to…
The Energy Cost of Execution-Idle in GPU Clusters
Yiran Lei, Jared Fernandez, Vasilis Kypriotis +4
GPUs are becoming a major contributor to data center power, yet unlike CPUs, they can remain at high power even when visible activity is near zero. We call this state execution-idl…
Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation
Alex C. Newkirk, Jared Fernandez, Jonathan Koomey +4
As AI's energy demand continues to grow, it is critical to enhance the understanding of characteristics of this demand, to improve grid infrastructure planning and environmental as…
Energy Considerations of Large Language Model Inference and Efficiency Optimizations
Jared Fernandez, Clara Na, Vashisth Tiwari +3
As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on laten…
Holistically Evaluating the Environmental Impact of Creating Language Models
Jacob Morrison, Clara Na, Jared Fernandez +3
As the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems. While many model developers release…
Gradient Localization Improves Lifelong Pretraining of Language Models
Jared Fernandez, Yonatan Bisk, Emma Strubell
Large Language Models (LLMs) trained on web-scale text corpora have been shown to capture world knowledge in their parameters. However, the mechanism by which language models store…
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
Jared Fernandez, Luca Wehrstedt, Leonid Shamis +5
Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To devel…
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Hao Peng, Qingqing Cao, Jesse Dodge +11
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental co…
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
Jared Fernandez, Jacob Kahn, Clara Na +2
Increased focus on the computational efficiency of NLP systems has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. Howev…
CIGLI: Conditional Image Generation from Language & Image
Xiaopeng Lu, Lynnette Ng, Jared Fernandez +1
Multi-modal generation has been widely explored in recent years. Current research directions involve generating text based on an image or vice versa. In this paper, we propose a ne…
Generative Data Augmentation for Commonsense Reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez +6
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance. However, manual curation of training examples is expensive…
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
Michael Chen, Mike D'Arcy, Alisa Liu +2
Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on…