-
Notifications
You must be signed in to change notification settings - Fork 157
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[onert-micro] Release Note for 2.0.0-pre (#13334)
- Release Note for onert-micro 2.0.0-pre ONE-DCO-1.0-Signed-off-by: Chunseok Lee <[email protected]>
- Loading branch information
1 parent
4b24efa
commit e3929a0
Showing
1 changed file
with
14 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,14 @@ | ||
## Release Notes for onert-micro 2.0.0-pre | ||
|
||
### Overall Structure Refactored | ||
|
||
- c++ api has been changed : [onert-micro c++ api](https://github.com/samsung/ONE/blob/master/onert-micro/onert-micro/include/OMInterpreter.h) | ||
- 60 ops supported : Abs, Add, AddN, AveragePool2D, ArgMax, ArgMin, Concatenation, BatchToSpaceD, Cos, Div, DepthwiseCov2D, Dequatize, FullyCoected, Cov2D, Logistic, Log, Gather, GatherD, Exp, Greater, GreaterEqual, ExpadDims, Equal, Floor, FloorDiv, FloorMod, Pad, Reshape, ReLU, ReLU6, Roud, Less, L2ormalize, L2Pool2D, LessEqual, LeakyReLU, LogSoftmax, Mul, Maximum, MaxPool2D, Miimum, otEqual, Si, SquaredDifferece, Slice, Sub, Split, SpaceToBatchD, StridedSlice, Square, Sqrt, SpaceToDepth, Tah, Traspose, TrasposeCov, Softmax, While, Rsqrt, Upack | ||
|
||
### onert-micro supports on-device training feature | ||
|
||
- Trainable Operations : 5 operations ( Conv2D, FullyConnected, MaxPool2D, Reshape, Softmax ) | ||
- Loss : MSE, Categorical Cross Entropy | ||
- Optimizer : ADAM, SGD | ||
- C api for training feature : [onert-micro c api header](https://github.com/samsung/ONE/blob/master/onert-micro/onert-micro/include/onert-micro.h) | ||
- limitation : For now, you can train topologically linear model |