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High-Performance Simulation of Spiking Neural Network on GPUs
Exploit sparsity of data flow in spiking neural network simulations
Keywords: Spiking neural networks, sparse
One major complication in research of biologically-inspired spiking neural
Networks (SNNs) is simulation performance on conventional hardware (CPU/GPU). Computation in SNNs is dominated by operations on sparse tensors but usually this potential benefit is ignored to save development time. However, the exploitation of sparsity could be beneficial to scale simulation of SNNs to larger datasets.
Requirements:
- Experience with deep learning frameworks (e.g. TensorFlow or PyTorch)
- Excellent programming skills and experience in CUDA
One major complication in research of biologically-inspired spiking neural Networks (SNNs) is simulation performance on conventional hardware (CPU/GPU). Computation in SNNs is dominated by operations on sparse tensors but usually this potential benefit is ignored to save development time. However, the exploitation of sparsity could be beneficial to scale simulation of SNNs to larger datasets.
Requirements:
- Experience with deep learning frameworks (e.g. TensorFlow or PyTorch) - Excellent programming skills and experience in CUDA
In this project, you will leverage sparse computation to develop high-performance simulations of SNNs that can be used for optimization. This will help to scale experiments and drastically improve results obtained by SNNs.
In this project, you will leverage sparse computation to develop high-performance simulations of SNNs that can be used for optimization. This will help to scale experiments and drastically improve results obtained by SNNs.