Engineering topics

Hayne, Lucas

Publications and source records attributed to Hayne, Lucas.

4 records

DOE OSTI2022

Green AI: Insights Into Deep Learning's Looming Energy Efficiency Crisis

As demands grow to integrate artificial intelligence into every aspect of industry, commerce, and life, deep learning's exploding energy cost has become a looming crisis, making AI systems a salient energy-efficiency challenge. One might expect that doubling a neural network's size would halve its error rate, or at least allow it to achieve greater performance given the same amount of time and energy. I will present clear and substantial scientific evidence which indicates that not only is this intuition wildly wrong, but that neural networks scale so poorly that to increase deep learning performance by only a small fraction can easily require an order of magnitude or more increase in computational resources and energy. Further, the marginal trade-off price of to increase model performance rapidly explodes as performance targets are increased. To address this challenge, I will provide a toolkit of techniques that can be applied today to mitigate the inefficiency of modern deep learning. And, I will conclude by illuminating a practical path forward towards efficient, Green AI.

artificial intelligence↗

DOE OSTI2022

BUTTER - Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Array↗

DOE OSTI2022

JobQueue-PG: A Task Queue for Coordinating Varied Tasks Across Multiple HPC Resources and HPC Jobs

The software allows for queueing and dispatch of tasks of small, varied, or uncertain runtimes across multiple HPC jobs, resources, and other computing systems. The software was designed to allow scientists to enqueue, run, and accumulate results from computational experiments in an efficient, manageable manner. For example, the software can be used to enqueue many small computational experiments and run them using several long-running multi-node HPC jobs that may or may not run simultaneously.

Tripp, Charles↗

DOE OSTI2022

BUTTER: An Empirical Deep Learning Experimental Framework [SWR-22-42]

BUTTER ( An Empirical Deep Learning Experimental Framework) is a system with which empirical deep learning experiments can be conducted and their results, including training and model performance characteristics, can be accumulated for further analysis.

Tripp, Charles↗