PARALLELISATION OF DFA ALGORITHM FOR DEEP NEURAL NETWORK TRAINING
DOI:
https://doi.org/10.24867/21BE04GrahovacKeywords:
DFA, deep neural networks, multiprocessorsAbstract
This paper presents a multiprocessor parallelization of the DFA algorithm for neural network training. Parallelization is implemented for a deep neural network of arbitrary dimensions.
References
[1] Nøkland A., “Direct Feedback Alignment Provides Learning in Deep Neural Networks”, 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain.
[2] OpenAI, “Language Models are Few-Shot Learners”, Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
[3] Grother P. J., “MNIST Special Database 19”, National Institute of Standards and Technology, 1995.
[4] Launay J., Poli I., Boniface F., Krzakala F., “Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures”, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.
[2] OpenAI, “Language Models are Few-Shot Learners”, Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
[3] Grother P. J., “MNIST Special Database 19”, National Institute of Standards and Technology, 1995.
[4] Launay J., Poli I., Boniface F., Krzakala F., “Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures”, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.
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Published
2023-01-07
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Section
Electrotechnical and Computer Engineering
