FlashKAT: Understanding and Addressing Performance Bottlenecks in the Kolmogorov-Arnold Transformer

The challenges faced by the Kolmogorov-Arnold Transformer (KAT) highlight broader trends in AI model performance, particularly in training speed. While KAT offers comparable FLOPs to traditional Transformers, its training speed remains significantly hindered, being 123 times slower. This aligns with findings in related research, such as the work on enhancing training speeds in recursive models, which emphasizes the importance of optimizing training processes. The exploration of memory stalls and gradient accumulation inefficiencies in KAT resonates with ongoing efforts in the field to improve model efficiency, as seen in the SAMora project, which seeks to enhance model performance through innovative training techniques.