Publication: Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA
Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA
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Asgari, H., Maybodi, B. M.-N., Payvand, M., & Azghadi, M. R. (2020). Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA. IEEE Transactions on Circuits and Systems. Part 2: Express Briefs, 67(11), 2697–2701. https://doi.org/10.1109/tcsii.2020.2968588
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Supervised, unsupervised, and reinforcement learning (RL) mechanisms are known as the most powerful learning paradigms empowering neuromorphic systems. These systems typically take advantage of unsupervised learning because they can learn the distribution of sensory information. However, to perform a task, not only is it important to have sensory information, but also it is required to have information about the context in which the system is operating. In this sense, reinforcement learning is very powerful for interacting with the en
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- Asgari, Hajar
- Maybodi, Babak Mazloom-Nezhad
- Payvand, Melika
- Azghadi, Mostafa Rahimi
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Asgari, H., Maybodi, B. M.-N., Payvand, M., & Azghadi, M. R. (2020). Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA. IEEE Transactions on Circuits and Systems. Part 2: Express Briefs, 67(11), 2697–2701. https://doi.org/10.1109/tcsii.2020.2968588