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Data quantization is an effective method to accelerate neural networktraining and reduce power consumption. However, it is challenging to performlow-bit quantized training: the conventional equal-precision quantization willlead to either high accuracy loss or limited bit-width reduction, whileexisting mixed-precision methods offer high compression potential but failed toperform accurate and efficient bit-width assignment. In this work, we proposeDYNASTY, a block-wise dynamic-precision neural network training framework.DYNASTY provides accurate data sensitivity information through fast onlineanalytics, and maintains stable training convergence with an adaptive bit-widthmap generator. Network training experiments on CIFAR-100 and ImageNet datasetare carried out, and compared to 8-bit quantization baseline, DYNASTY brings upto $5.1\times$ speedup and $4.7\times$ energy consumption reduction with noaccuracy drop and negligible hardware overhead.

Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics