jtuyls commented on a change in pull request #14:
URL: https://github.com/apache/tvm-rfcs/pull/14#discussion_r683240728



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File path: rfcs/0012-pipeline-executor.md
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+- Feature Name: (fill me in with a unique identifier, `my_awesome_feature`)
+- Start Date: (fill me in with today's date, YYYY-MM-DD)
+- RFC PR: [apache/tvm-rfcs#0014](https://github.com/apache/tvm-rfcs/pull/0014)
+- GitHub Issue: [apache/tvm#8596](https://github.com/apache/tvm/issues/8596)
+
+## 1. Summary
+
+
+This proposal introduces Pipeline Executor: A runtime executor that by 
scheduling
+splitted subgraph of relay graph in pipeline to implement task level parallism 
to
+reduce compute latency.

Review comment:
       On point 2, with latency in ML we typically mean the time it takes to 
process one unit of data, which for image classification is one image if the 
batch size is 1. In the example given, I would argue that the batch size is 
still 1 instead of 2 as the data inputs are just being queued and executed 
separately and not being processed together. Therefore, the latency is not 
reduced but the throughput is improved.




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