@liangfu Thanks for your reply. I would assume some implementation is required
in the runtime to call APIs provided by Zephyr (e.g, memory allocation, etc).
Is there some base I can use as a start? I have several Arm boards with Zephyr
support on hand, so I can try the TVM support on these real
Hi @yangjunpro @hello-hzb ,
This project is suspended for several months. I won't continue my work on the
original branch.
However, the push for an auto-scheduler is still interesting to a lot of
people, I might work on auto-scheduler again with some Berkeley students. We'd
like to try different
@merrymercy would you mind summarize a bit what's the drawback of the original
implement, so we can learn from it.
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A significant driver of progress in deep learning has been advances in
computational resources. While those resources are often limited, the is a
trend to replace dense computation in DNN with sparse computation for speeding
up / saving memory to enable larger models. For example: [neural netwo
Welcome comment and discussion! @cylinbao @yuluny2 @tmoreau89 @Huyuwei @tqchen
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cc @sf-wind
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We need to update
[benchmarks](https://github.com/apache/incubator-tvm/tree/master/apps/benchmark)
to use Relay, before we deprecate NNVM.
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