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Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, hardware, and algorithm-to-hardware mapping, a.k.a. dataflow. However, owing to the large joint design space, finding an optimal solution through physical implementation becomes infeasible. To tackle this problem, several design space exploration (DSE) frameworks have emerged recently, yet they either suffer from long runtimes or a limited exploration space. This article introduces ZigZag, a rapid DSE framework for DNN accelerator architecture and mapping. ZigZag extends the common DSE with uneven mapping opportunities and smart mapping search strategies. Uneven mapping decouples operands (W/I/O), memory hierarchy, and mappings (temporal/spatial), opening up a whole new space for DSE, and thus better design points are found by ZigZag compared to other SotAs. For this, ZigZag uses an …