TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT
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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 ResNet-18 models.
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