You can now quantize neural networks with better geometric information (two-sided Hessian) at roughly the same computational cost as simpler one-sided methods, enabling more accurate model compression.
BaKron speeds up neural network quantization by efficiently using two-sided Hessian information to round weights more intelligently. Instead of the expensive O(m²n²) computation, it achieves O(mn(m+n)) work while maintaining the same cubic scaling as existing methods, making it practical to apply richer curvature information during quantization.