Neuromorphic hardware platforms can significantly lower the energy overh...
Recently, both industry and academia have proposed several different
neu...
As spiking-based deep learning inference applications are increasing in
...
Spiking Neural Networks (SNN) are an emerging computation model, which u...
Neuromorphic computing systems uses non-volatile memory (NVM) to impleme...
Recently, both industry and academia have proposed many different
neurom...
Neuromorphic computing systems such as DYNAPs and Loihi have recently be...
Spiking Neural Networks (SNNs) are efficient computation models to perfo...
With growing model complexity, mapping Spiking Neural Network (SNN)-base...
In this paper, we propose a design methodology to partition and map the
...
Machine learning applications that are implemented with spike-based
comp...
We present PyCARL, a PyNN-based common Python programming interface for
...
Neuromorphic hardware with non-volatile memory (NVM) can implement machi...
Neuromorphic hardware platforms implement biological neurons and synapse...