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
Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory computing are the two dominant design trends of neuromorphic processors. However, there remain challenges in reducing the overhead of event-driven processing and increasing the mapping efficiency of near/in-memory computing, which directly impacts the performance and area efficiency...
2024: Frontiers in Neuroscience