Peizhe Wang

MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network

Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and difficulties in model parallelization during training. Recent research has sought to address these issues, with one promising…