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A new open-source, cycle-accurate architectural simulator, STONNE, allows for detailed evaluation of flexible deep neural network accelerators. The simulator validated against MAERI, an existing flexible design, identified that MAERI's folding strategy led to an average compute unit utilization of only 25% across various DNNs, suggesting significant performance gains with an optimized approach.