Search Results for author:
Found 6 papers, 0 papers with code
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa.
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging.
Attention-Based Structural-Plasticity
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks.
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware.
A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems.
