Publication: FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation
FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation
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Chen, Q., Sun, C., Gao, C., Fang, X., & Luan, H. (2023). FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation. Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems, 1–5. https://doi.org/10.1109/aicas57966.2023.10168660
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Fast video recognition is essential for real-time scenarios, e.g., autonomous driving. However, applying existing Deep Neural Networks (DNNs) to individual high-resolution images is expensive due to large model sizes. Spiking Neural Networks (SNNs) are developed as a promising alternative to DNNs due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus they are useful to enable energy-efficient computation. However, exploiting the spatio-temporal sparsi
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Chen, Q., Sun, C., Gao, C., Fang, X., & Luan, H. (2023). FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation. Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems, 1–5. https://doi.org/10.1109/aicas57966.2023.10168660
