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Low-precision DNNs have been extensively explored in order to reduce the sizeof DNN models for edge devices. Recently, the posit numerical format has shownpromise for DNN data representation and compute with ultra-low precision in[5..8]-bits. However, previous studies were limited to studying posit for DNNinference only. In this paper, we propose the Cheetah framework, which supportsboth DNN training and inference using posits, as well as other commonly usedformats. Additionally, the framework is amenable for different quantizationapproaches and supports mixed-precision floating point and fixed-pointnumerical formats. Cheetah is evaluated on three datasets: MNIST, FashionMNIST, and CIFAR-10. Results indicate that 16-bit posits outperform 16-bitfloating point in DNN training. Furthermore, performing inference with[5..8]-bit posits improves the trade-off between performance andenergy-delay-product over both [5..8]-bit float and fixed-point.

Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge