Patents by Inventor Michael Beyeler

Michael Beyeler has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11636317
    Abstract: Long-short term memory (LSTM) cells on spiking neuromorphic hardware are provided. In various embodiments, such systems comprise a spiking neurosynaptic core. The neurosynaptic core comprises a memory cell, an input gate operatively coupled to the memory cell and adapted to selectively admit an input to the memory cell, and an output gate operatively coupled to the memory cell an adapted to selectively release an output from the memory cell. The memory cell is adapted to maintain a value in the absence of input.
    Type: Grant
    Filed: February 16, 2017
    Date of Patent: April 25, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Rathinakumar Appuswamy, Michael Beyeler, Pallab Datta, Myron Flickner, Dharmendra S. Modha
  • Publication number: 20180232631
    Abstract: Long-short term memory (LSTM) cells on spiking neuromorphic hardware are provided. In various embodiments, such systems comprise a spiking neurosynaptic core. The neurosynaptic core comprises a memory cell, an input gate operatively coupled to the memory cell and adapted to selectively admit an input to the memory cell, and an output gate operatively coupled to the memory cell an adapted to selectively release an output from the memory cell. The memory cell is adapted to maintain a value in the absence of input.
    Type: Application
    Filed: February 16, 2017
    Publication date: August 16, 2018
    Inventors: Rathinakumar Appuswamy, Michael Beyeler, Pallab Datta, Myron Flickner, Dharmendra S. Modha
  • Publication number: 20170213134
    Abstract: Example embodiments for efficient neuromorphic population coding are described. In one case, individual instances of input stimuli are evaluated using a set of feature encoding units to generate a population of encoded feature values. The population of encoded values for each of the individual input stimuli are arranged into a population code matrix. The population code matrix is factorized into a basis element matrix and a contribution coefficient matrix based on a number of basis vectors, where the number of basis vectors is selected to balance sparseness in the basis element matrix and reconstruction error of the population code matrix from the basis element matrix and the contribution coefficient matrix. The embodiments are compatible with neuromorphic hardware and can achieve compact representation of high-dimensional data, infer latent variables in the data, and defer processing to an off-line training phase to save time during real-time data capture and evaluation.
    Type: Application
    Filed: January 27, 2017
    Publication date: July 27, 2017
    Inventors: Michael Beyeler, Nikil D. Dutt, Jeffrey L. Krichmar