Neuromorphic Computing in the Era of Large Models

The rapid advancement of deep learning and the emergence of large-scale neural models, such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformer (GPT), and large language model Meta AI (LLaMa), have brought significant computational and energy challenges. Neuromorphic computing presents a biologically inspired approach to addressing these issues, leveraging event-driven processing and in-memory computation for enhanced energy efficiency. This survey explores the intersection of neuromorphic computing and large-scale deep learning models, focusing on neuromorphic models, learning methods, and hardware. We highlight transferable techniques from deep learning to neuromorphic computing and examine the memory-related scalability limitations of current neuromorphic systems. Furthermore, we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.