1.1k citations · 2.2k across the 33 of their papers we have counts for
6 papers · 1 filter
Delta Networks for Optimized Recurrent Network Computation
Daniel Neil, Jun Haeng Lee, Tobi Delbruck +1
Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this prop…
Recurrent Neural Networks With Limited Numerical Precision
Joachim Ott, Zhouhan Lin, Ying Zhang +2
Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often…
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irre…
Recurrent Neural Networks With Limited Numerical Precision
Joachim Ott, Zhouhan Lin, Ying Zhang +2
Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often…
Precise neural network computation with imprecise analog devices
Jonathan Binas, Daniel Neil, Giacomo Indiveri +2
The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency. Neve…
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition
Stefan Braun, Daniel Neil, Shih-Chii Liu
The performance of automatic speech recognition systems under noisy environments still leaves room for improvement. Speech enhancement or feature enhancement techniques for increas…