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Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces
Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained …
Paul Hueber, Guangzhi Tang, Manolis Sifalakis, Hua-Peng Liaw, Aurora Micheli, Nergis Tomen, Yao-Hong Liu
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Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration
Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, …
Yingfu Xu, Kevin Shidqi, Gert-Jan van Schaik, Refik Bilgic, Alexandra Dobrita, Shenqi Wang, Roy Meijer, Prithvish Nembhani, Cina Arjmand, Pietro Martinello, Anteneh Gebregiorgis, Said Hamdioui, Paul Detterer, Stefano Traferro, Mario Konijnenburg, Kanishkan Vadivel, Manolis Sifalakis, Guangzhi Tang, Amirreza Yousefzadeh
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SENECA: Building a fully digital neuromorphic processor, design trade-offs and challenges
Neuromorphic processors aim to emulate the biological principles of the brain to achieve high efficiency with low power consumption. …
Guangzhi Tang, Kanishkan Vadivel, Yingfu Xu, Refik Bilgic, Kevin Shidqi, Paul Detterer, Stefano Traferro, Mario Konijnenburg, Manolis Sifalakis, Gert-Jan van Schaik, Amirreza Yousefzadeh
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Decoding EEG With Spiking Neural Networks on Neuromorphic Hardware
Decoding motor activity accurately and reliably from electroencephalography (EEG) signals is essential for several portable …
Neelesh Kumar, Guangzhi Tang, Raymond Yoo, Konstantinos Michmizos
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A Spiking Neural Network Mimics the Oculomotor System to Control a Biomimetic Robotic Head without Learning on a Neuromorphic Hardware
Facilitated by the emergence of neuromorphic hardware, neuromorphic algorithms mimic the brain’s asynchronous computation to improve …
Ioannis Polykretis, Guangzhi Tang, Praveenram Balachandar, Konstantinos Michmizos
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