MERSIT: A Hardware-Efficient 8-bit Data Format with Enhanced Post-Training Quantization DNN Accuracy

Post-training quantization (PTQ) models utilizing conventional 8-bit Integer or floating-point formats still exhibit significant accuracy drops in modern deep neural networks (DNNs), rendering them unreliable. This paper presents MERSIT, a novel 8-bit PTQ data format designed for various DNNs. While leveraging the dynamic configuration of exponent and fraction bits derived from Posit data format, MERSIT demonstrates enhanced hardware efficiency through the proposed merged decoding scheme. Our evaluation indicates that MERSIT yields more reliable 8-bit PTQ models, exhibiting superior accuracy across various DNNs compared to conventional floating-point formats. Furthermore, the proposed processing unit saves 26.6% in area and 22.2% in power consumption compared to the Posit-based unit, while maintaining comparable efficiency to the floating-point-based unit.