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Streaming Batch Gradient Tracking for Neural Network Training



Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels, Mark D. Stiles, Gina C. Adam


Faster and more energy efficient hardware accelerators are critical for machine learning on very large datasets. The energy cost of performing vector-matrix multiplication and repeatedly moving neural network models in and out of memory motivates a search for alternative hardware and algorithms. We propose to use streaming batch principal component analysis (SBPCA) to compress batch data during training by using a rank-k approximation of the total batch update. This approach yields comparable training performance to minibatch gradient descent (MBGD) at the same batch size while reducing overall memory and compute requirements.
Conference Dates
February 7-12, 2020
Conference Location
New York, NY
Conference Title
AAAI Conference on Artificial Intelligence


Huang, S. , Hoskins, B. , Daniels, M. , Stiles, M. and Adam, G. (2020), Streaming Batch Gradient Tracking for Neural Network Training, AAAI Conference on Artificial Intelligence, New York, NY, [online], (Accessed March 2, 2024)
Created April 2, 2020, Updated July 8, 2020