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    How does Taso optimize the computation graphs of DNN models?TASO optimizes the computation graphs of DNN models using automatically generated and verified graph transformations. For an arbitrary DNN model, TASO uses the auto-generated graph transformations to build a large search space of potential computation graphs that are equivalent to the original DNN model.
    How many tensors does Taso use?The two intermediate tensors in the target graph can have either a row-major or a column-major layout, therefore TASO considers four diferent computation graphs (i.e., CC, CR, RC, and RR for the two intermediate ten-sors) when applying this substitution. This allows TASO to Figure 6.
    Can Taso optimize both graph structures and data layouts?As a result, TASO can automatically generate graph substitu-tions that optimize both graph structures and data layouts, and our evaluation shows that jointly optimizing the two tasks can significantly improve the end-to-end performance, compared to optimizing the them separately.
    How fast does Taso outperform the three baseline strategies?TASO outperforms the three baseline strategies by 1.2-1.3×. We observe that the speedup is achieved by using graph substitutions that transform both graph struc-ture and data layout. One example is depicted in Figure 5. The most time consuming operation in BERT is matrix mul-tiplication A× B, where A is 64 by 1024 and B is 1024 by 4096.
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