Rishav Mukherji

Apr 25, 2024 | fis.tu-dresden.de | Khaleelulla Khan Nazeer |Mark Schone |Rishav Mukherji |Science Pilani

As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential way to reduce energy consumption for inference significantly. However, to date, most event-based networks that can run on neuromorphic hardware, including spiking neural networks (SNNs), have not achieved task performance even on par with LSTM models for language modeling.