Daniel Neil

Position:
PhD Student -- ended May 2017
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Daniel Neil received his B.S. degree in Biomedical Computation from Stanford and minored there in Chinese and Japanese. He completed his master’s degree in the Neural Systems and Computation program from the Institute of Neuroinformatics and is currently pursuing his Doctoral degree at the INI. Formerly, he was a research assistant in Kwabena Boahen’s Brains in Silicon Laboratory at Stanford and helped to build Neurogrid (the lowest-power neuron supercomputer), and worked as a technical consultant in the San Francisco Bay Area. He is also a co-founder of Ponder (www.pondertalks.com), a site to discover intellectual events.

Currently, his research interests focus on discovering scalable architectures for advanced machine learning, with a focus on deep neural networks. Specifically, he focuses on analyzing and building neuron models and hardware architectures that support efficient processing of deep neural networks, convolutional neural networks, and recurrent neural networks.

Publications

2018

  • Anumula, J., Neil, D., Delbruck, T., and Liu, S-C. Feature representations for neuromorphic audio spike streams, Front. Neurosci.: Special Issue on Bio-Inspired Audio Processing, Models and Systems, 2018
  • Diederik Paul Moeys, Daniel Neil, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Sonya A. Coleman, Thomas M. McGinnity, Dermot Kerr, Tobi Delbruck PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing, IEEE Fourth International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP), 2018 pdf
  • Gao, C., Neil, D., Ceolini, E., Liu, S-C., Delbruck, T. DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator, Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA 2018) 21-30, 2018

2017

2016

2015

2014

2013

© 2018 Institut für Neuroinformatik