Now showing items 1-3 of 3

    • Boosting LSTM performance through dynamic precision selection 

      Silfa Feliz, Franyell Antonio; Arnau Montañés, José María; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2020)
      Conference report
      Open Access
      The use of low numerical precision is a fundamental optimization included in modern accelerators for Deep Neural Networks (DNNs). The number of bits of the numerical representation is set to the minimum precision that is ...
    • E-PUR: an energy-efficient processing unit for recurrent neural networks 

      Silfa Feliz, Franyell Antonio; Dot, Gem; Arnau Montañés, José María; González Colás, Antonio María (2018)
      Conference report
      Restricted access - publisher's policy
      Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful ...
    • Neuron-level fuzzy memoization in RNNs 

      Silfa Feliz, Franyell Antonio; Dot Artigas, Gem; Arnau Montañés, José María; González Colás, Antonio María (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the ...