GPUs became the reference platform for both training and inference phases of Convolutional Neural Networks (CNN), due to their tailored architecture to the CNN operators. However, GPUs are power-hungry architectures. A path to enable the deployment of CNNs in energy-constrained devices is adopting hardware accelerators for the inference phase. The design space exploration of CNNs using standard approaches, such as RTL, is limited due to their complexity. Thus, designers need frameworks enabling design space exploration that delivers accurate hardware estimation metrics do deploy CNNs. This work aims to propose a framework to explore hardware accelerators' design space, providing power, performance, and area (PPA) estimations. The heart of the framework is a system simulator with TensorFlow as front-end and as back-end performance estimations obtained from the physical synthesis. Results evaluate the energy trade-off varying the number of convolutional layers, showing that it can be necessary to spend approximately 40% more energy to increase 0.02% in the accuracy.

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