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Editorial: Link to error handling proposal from the MLTensor explainer #785

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Editorial: Link to error handling proposal from the MLTensor explainer #785

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3 changes: 2 additions & 1 deletion mltensor-explainer.md
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Expand Up @@ -292,7 +292,8 @@ It's possible `compute()` may have a performance advantage on some platforms for

### Open Questions

- How will errors be surfaced? Do we need a concept similar to [WebGPU's error scopes](https://www.w3.org/TR/webgpu/#error-scopes), or is [returning errors via a promise for select operations](https://github.com/webmachinelearning/webnn/issues/697#issuecomment-2195656878) and losing the `MLContext` sufficient? See [#477](https://github.com/webmachinelearning/webnn/issues/477)
- How will errors be surfaced? Do we need a concept similar to [WebGPU's error scopes](https://www.w3.org/TR/webgpu/#error-scopes), or is [returning errors via a promise for select operations](https://github.com/webmachinelearning/webnn/issues/697#issuecomment-2195656878) and losing the `MLContext` sufficient?
- *Update: [#778](https://github.com/webmachinelearning/webnn/issues/778) is a proposal for reporting non-fatal errors from the WebNN timeline*
- Does the user agent have enough information to appropriately allocate an `MLTensor` if an `MLDeviceType` or `GPUDevice` is not used to create an `MLContext`? See [#350](https://github.com/webmachinelearning/webnn/issues/350) and [#749](https://github.com/webmachinelearning/webnn/issues/749)
- Should the `dispatch()` method be a part of the `MLGraph` interface rather than `MLContext`? Should `readTensor()` and `writeTensor()` exist on an `MLTensor`? See [#697](https://github.com/webmachinelearning/webnn/issues/697).
- Is a sync variant of the `importExternalBuffer()` method feasible (1) on platforms where completion of ML compute can be signaled on a GPU timeline, or (2) when blocking WebGPU workloads which do not themselves block compositing.
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