  In this research, we propose a new low-precision framework, TENT, to leverage
the benefits of a tapered fixed-point numerical format in TinyML models. We
introduce a tapered fixed-point quantization algorithm that matches the
numerical format's dynamic range and distribution to that of the deep neural
network model's parameter distribution at each layer. An accelerator
architecture for the tapered fixed-point with TENT framework is proposed.
Results show that the accuracy on classification tasks improves up to ~31 %
with an energy overhead of ~17-30 % as compared to fixed-point, for ConvNet and
ResNet-18 models.
