Kernekoncepter
NeuroFlux introduces adaptive local learning for memory-efficient CNN training, showcasing speed-ups and streamlined models compared to Backpropagation.
Resumé
NeuroFlux presents a novel approach to memory-constrained CNN training. By segmenting the CNN into blocks and employing adaptive strategies, it accelerates training and reduces parameters. The system caches intermediate activations, eliminating redundant forward passes. NeuroFlux outperforms traditional methods in terms of speed and efficiency.
Statistik
NeuroFlux demonstrates training speed-ups of 2.3× to 6.1× under stringent GPU memory budgets.
NeuroFlux generates streamlined models with 10.9× to 29.4× fewer parameters.