Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

  title={Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware},
  author={S. Liu and D. Mocanu and Amarsagar Reddy Ramapuram Matavalam and Y. Pei and M. Pechenizkiy},
  journal={Neural Computing and Applications},
  • S. Liu, D. Mocanu, +2 authors M. Pechenizkiy
  • Published 2020
  • Computer Science, Mathematics
  • Neural Computing and Applications
  • Artificial neural networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very high dimensionality and the small number of samples make it difficult for machine learning techniques to handle. Furthermore, specialized hardware such as graphics processing unit… CONTINUE READING
    11 Citations
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