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Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. …
Guangzhi Tang, Ali Safa, Kevin Shidqi, Paul Detterer, Stefano Traferro, Mario Konijnenburg, Manolis Sifalakis, Gert-Jan van Schaik, Amirreza Yousefzadeh
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Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
The energy-efficient control of mobile robots has become crucial as the complexity of their real-world applications increasingly …
Guangzhi Tang, Neelesh Kumar, Raymond Yoo, Konstantinos Michmizos
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Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware
Energy-efficient mapless navigation is crucial for mobile robots as they explore unknown environments with limited on-board resources. …
Guangzhi Tang, Neelesh Kumar, Konstantinos Michmizos
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Spiking neural network on neuromorphic hardware for energy-efficient unidimensional SLAM
Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The …
Guangzhi Tang, Arpit Shah, Konstantinos Michmizos
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