Closed mydmdm closed 1 year ago
Hello,
the paper mentions methods for:
Will these be added to the repository ? If they will be added: Do you have a rough time frame for when they will be available ?
Hello,
the paper mentions methods for:
- detecting the fusion rules on a device
- adaptive sampling for creating the latency dataset
Will these be added to the repository ? If they will be added: Do you have a rough time frame for when they will be available ?
@gmimsgt, Hi, we plan to add the fusion rule detection and adaptive sampling algorithms. We will start after version 1.0-beta finishes.
@Lynazhang Thanks for the quick answer.
I appreciate the effort put into polishing the code base as it allowed me to get started quickly. Especially the fusion rule detection and adaptive sampling are very interesting as I am currently trying to predict/benchmark a new device. The paper has been very helpful in this regard and I would love to try out the implementation.
If it is not an inconvenience is it possible to get the current state of the code?
Hi, I'm wondering if you would share your modification code to TFLite, which implements the GPU operator-level profiling?
Hi, I'm wondering if you would share your modification code to TFLite, which implements the GPU operator-level profiling?
Hi, @liuyibox, we will soon share a patch about the GPU operator-level profiling.
nn-Meter
is not only a latency predictor but also a critical component in the hardware-aware model design. It empowers existing NAS (neural architecture search) and other efficient model design tasks to be specialized for the target hardware platform.There are multiple aspects will be covered in this and related repo, including:
Release Plan
version 1.0-alpha
version 1.0-beta
SPOS: first integrate nn-meter in the evolution search(move to 2.0)version 2.0
NovemberDecember