Zobrazeno 1 - 10
of 32
pro vyhledávání: '"J. Parker Mitchell"'
Publikováno v:
IEEE Transactions on Control Systems Technology. 30:2433-2449
Autor:
Catherine D. Schuman, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Prasanna Date, Bill Kay
Publikováno v:
Nature Computational Science. 2:10-19
Publikováno v:
Neurocomputing. 447:145-160
Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow
Publikováno v:
ICONS
Neuromorphic computing is emerging as a promising Beyond Moore computing paradigm that employs event-triggered computation and non-von Neumann hardware. Spike Timing Dependent Plasticity (STDP) is a well-known bio-inspired learning rule that relies o
Publikováno v:
ICONS
The training process for spiking neural networks can be very computationally intensive. Approaches such as evolutionary algorithms may require evaluating thousands or millions of candidate solutions. In this work, we propose using neuromorphic cores
Autor:
Maryam Parsa, Catherine D. Schuman, Shay E. Snyder, Thomas E. Potok, Shruti R. Kulkarni, Christopher G. Stahl, N. Quentin Haas, Spencer Paulissen, Prasanna Date, J. Parker Mitchell, Robert M. Patton
Publikováno v:
ICONS
Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of
Autor:
Kaushik Roy, Bill Kay, Amir Ziabari, Steven R. Young, J. Parker Mitchell, Nitin Rathi, Catherine D. Schuman, Derek C. Rose, Maryam Parsa, Travis Johnston
Publikováno v:
ICONS
Neuromorphic systems allow for extremely efficient hardware implementations for neural networks (NNs). In recent years, several algorithms have been presented to train spiking NNs (SNNs) for neuromorphic hardware. However, SNNs often provide lower ac
Autor:
James S. Plank, Catherine D. Schuman, Nicholas D. Skuda, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell
Publikováno v:
IJCNN
There are a wide variety of training approaches for spiking neural networks for neuromorphic deployment. However, it is often not clear how these training algorithms perform or compare when applied across multiple neuromorphic hardware platforms and
Autor:
Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Jeffrey K. Bassett, Mark Coletti, Catherine D. Schuman
Publikováno v:
CEC
Neuroevolution has had significant success over recent years, but there has been relatively little work applying neuroevolution approaches to spiking neural networks (SNNs). SNNs are a type of neural networks that include temporal processing componen
Publikováno v:
ACC
Dilute combustion using exhaust gas recirculation (EGR) is a cost-effective method for increasing engine efficiency. At high EGR levels, however, its efficiency benefits diminish as cycle-to-cycle variability (CCV) intensifies. In this simulation stu