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Found 5 papers, 0 papers with code
Hardware-friendly Neural Network Architecture for Neuromorphic Computing
The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such as the IBM Truenorth and Intel Loihi.
RRAM based neuromorphic algorithms
This submission is a report on RRAM based neuromorphic algorithms.
MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses
Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in crossbar architecture and also algorithms specific or compatible to neuromorphic hardware.
Classifying neuromorphic data using a deep learning framework for image classification
Since then, several neuromorphic datasets as obtained by applying such sensors on image datasets (e. g. the neuromorphic CALTECH 101) have been introduced.
Triplet Spike Time Dependent Plasticity: A floating-gate Implementation
Synapse plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP).
