Neuromorphic graph algorithms: Extracting longest shortest paths and minimum spanning trees
Neuromorphic computing is poised to become a promising computing paradigm in the post
Moore's law era due to its extremely low power usage and inherent parallelism. Traditionally …
Moore's law era due to its extremely low power usage and inherent parallelism. Traditionally …
Computing with spikes: The advantage of fine-grained timing
SJ Verzi, F Rothganger, OD Parekh, TT Quach… - Neural …, 2018 - direct.mit.edu
Neural-inspired spike-based computing machines often claim to achieve considerable
advantages in terms of energy and time efficiency by using spikes for computation and …
advantages in terms of energy and time efficiency by using spikes for computation and …
A spike-timing neuromorphic architecture
AJ Hill, JW Donaldson, FH Rothganger… - 2017 IEEE …, 2017 - ieeexplore.ieee.org
Unlike general purpose computer architectures that are comprised of complex processor
cores and sequential computation, the brain is innately parallel and contains highly complex …
cores and sequential computation, the brain is innately parallel and contains highly complex …
A novel digital neuromorphic architecture efficiently facilitating complex synaptic response functions applied to liquid state machines
Information in neural networks is represented as weighted connections, or synapses,
between neurons. This poses a problem as the primary computational bottleneck for neural …
between neurons. This poses a problem as the primary computational bottleneck for neural …
Spiking neural streaming binary arithmetic
Boolean functions and binary arithmetic operations are central to standard computing
paradigms. Accordingly, many advances in computing have focused upon how to make …
paradigms. Accordingly, many advances in computing have focused upon how to make …
Development of a Stand‐Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and …
This research presents an independent stand‐alone graphical computational tool which
functions as a neurological disease prediction framework for diagnosis of neurological …
functions as a neurological disease prediction framework for diagnosis of neurological …
A spiking neural algorithm for the network flow problem
It is currently not clear what the potential is of neuromorphic hardware beyond machine
learning and neuroscience. In this project, a problem is investigated that is inherently difficult …
learning and neuroscience. In this project, a problem is investigated that is inherently difficult …
Neural-inspired anomaly detection
Anomaly detection is an important problem in various fields of complex systems research
including image processing, data analysis, physical security and cybersecurity. In image …
including image processing, data analysis, physical security and cybersecurity. In image …
Memristors learn to play
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Neural Algorithms for Low Power Implementation of Partial Differential Equations.
The rise of low-power neuromorphic hardware has the potential to change high-
performance computing; however much of the focus on brain-inspired hardware has been …
performance computing; however much of the focus on brain-inspired hardware has been …