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In this research, we propose a new low-precision framework, TENT, to leveragethe benefits of a tapered fixed-point numerical format in TinyML models. Weintroduce a tapered fixed-point quantization algorithm that matches thenumerical format's dynamic range and distribution to that of the deep neuralnetwork model's parameter distribution at each layer. An acceleratorarchitecture 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 andResNet-18 models.

TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT