Beyond Bit-Width: Exploring Algorithmic Diversity in Neural Network Quantization

This research delves into CKA-guided modular quantization, suggesting a move away from solely focusing on bit-width to incorporate algorithmic diversity. The paper's contribution potentially offers improved performance and efficiency in quantized neural networks.

• •Focus shifts from solely reducing bit-width to incorporating algorithmic diversity in quantization.
• •The approach utilizes CKA-guided modular quantization.
• •Potential for improved performance and efficiency in quantized neural networks is suggested.