Imported Upstream version 1.21.0upstream/1.21.0tizen_7.0_m2_releaseaccepted/tizen/unified/20220912.170817accepted/tizen/unified/20220912.164738accepted/tizen/7.0/unified/hotfix/20221116.105341accepted/tizen/7.0/unified/20221110.060236tizen_7.0_hotfixtizen_7.0accepted/tizen_7.0_unified_hotfixaccepted/tizen_7.0_unified

6 files changed, 101 insertions, 14 deletions

diff --git a/docs/conf.py b/docs/conf.py
index 84197e6d6..409e5f79b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -21,7 +21,7 @@ copyright = '2020, Samsung Research & contributors'
 author = 'Samsung Research & contributors'
 
 # The full version, including alpha/beta/rc tags
-release = '1.20.0'
+release = '1.21.0'
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/docs/howto/how-to-build-runtime-tizen-gbs-rpi4.md b/docs/howto/how-to-build-runtime-tizen-gbs-rpi4.md
index 1f8c0c289..57b2b787c 100644
--- a/docs/howto/how-to-build-runtime-tizen-gbs-rpi4.md
+++ b/docs/howto/how-to-build-runtime-tizen-gbs-rpi4.md
@@ -174,34 +174,26 @@ $ vi j2/etc/systemd/system/ip.service
 and set as like:
 ```
 [Service]
-Type=simple
 Restart=always
 RestartSec=1
 User=root
-ExecStart=/bin/sh /bin/ip.sh
+ExecStart=/bin/sh -c "ifconfig eth0 192.168.x.y netmask 255.255.255.0 up"
 
 [Install]
 WantedBy=multi-user.target
 ```
+Replace 192.168.x.y to your actual ip address.
 
-(5-3) Add a new file
-```
-$ vi j2/bin/ip.sh
-```
-and set with IP address for your RPi4:
-```
-ifconfig eth0 192.168.x.y netmask 255.255.255.0 up
-```
-where you should update `192.168.x.y` part to your actual IP address.
 
-(5-4) Add a symbolic link
+(5-3) Add a symbolic link
 ```
+$ sudo mkdir -p j2/etc/systemd/system/multi-user.target.wants/
 $ pushd j2/etc/systemd/system/multi-user.target.wants/
 $ sudo ln -s ../../system/ip.service .
 $ popd
 ```
 
-(5-5) Now that every thing is ready, unmount and unplug your memory card and plug into
+(5-4) Now that every thing is ready, unmount and unplug your memory card and plug into
 RPi4, turn on the power.
 ```
 $ sync
diff --git a/docs/release/1.20/index.rst b/docs/release/1.20/index.rst
new file mode 100644
index 000000000..082d867f3
--- /dev/null
+++ b/docs/release/1.20/index.rst
@@ -0,0 +1,13 @@
+.. ONE documentation master file, created by
+   sphinx-quickstart on Tue Apr 26 10:18:12 2022.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+1.20
+====
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+  ./release-note-1.20.0.md
diff --git a/docs/release/1.20/release-note-1.20.0.md b/docs/release/1.20/release-note-1.20.0.md
new file mode 100644
index 000000000..2c75e06af
--- /dev/null
+++ b/docs/release/1.20/release-note-1.20.0.md
@@ -0,0 +1,34 @@
+# Release Note 1.20.0
+
+## ONE Compiler
+
+### Compiler Frontend
+
+- luci-interpreter supports multiple kernels with PAL layer including Cortext-M
+- luci-interpreter supports integer tensor for partly kernels
+- luci import support constant without coping to reduce memory for luci-interpreter
+- Reduce duplicate codes to package released modules
+- Limited support for ONNX LSTM/RNN unrolling while importing
+- Limited support for ARM32 cross build
+- Support new operator: SVDF
+- New virtual CircleVariable to support tensor with variable
+- Support quantization of BatchMatMul Op
+- Support mixed(UINT8 + INT16) quantization
+- Support backward propagation of quantization parameters
+- Upgrade default python to version 3.8
+- Support TensorFlow 2.8.0, ONNX-TF 1.10.0, ONNX 1.11.0
+- Upgrade circle schema to follow tflite schema v3b
+- Refactor to mio-tflite280, mio-circle04 with version and helpers methods
+- Use one flatbuffers 2.0 version
+- Drop support for TensorFlow 1.x
+- Fix for several bugs, performance enhancements, and typos
+
+## ONE Runtime
+
+### Introduce TRIX backend
+- TRIX backend supports trix binary with NHWC layout
+- TRIX backend supports trix binary with input/output of Q8 and Q16 type
+
+### API supports new data type
+- Symmetric Quantized int16 type named "NNFW_TYPE_TENSOR_QUANT16_SYMM_SIGNED"
+
diff --git a/docs/release/1.21/index.rst b/docs/release/1.21/index.rst
new file mode 100644
index 000000000..587065f56
--- /dev/null
+++ b/docs/release/1.21/index.rst
@@ -0,0 +1,13 @@
+.. ONE documentation master file, created by
+   sphinx-quickstart on Wed Sep 06 12:18:12 2022.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+1.21
+====
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+  ./release-note-1.21.0.md
diff --git a/docs/release/1.21/release-note_1.21.0.md b/docs/release/1.21/release-note_1.21.0.md
new file mode 100644
index 000000000..49bf074b6
--- /dev/null
+++ b/docs/release/1.21/release-note_1.21.0.md
@@ -0,0 +1,35 @@
+# Release Note 1.21.0
+
+## ONE Compiler
+
+- Support unrolling of LSTM and RNN Ops in `one-import-onnx` tool
+- Introduced new tools `one-infer`, `circle-operator`, `circle-interpreter`
+- Introduced `Workflow`(WIP) in `one-cmds`
+- New option `quant_config` in `one-quantize`
+- New option `fake_quantize` in `one-quantize`
+- More Ops supported: Densify
+- More Ops for quantization: ReduceMax
+- More Ops for mixed-precision quantization (MPQ): LeakyRelu, Neg, Relu6, Squeeze
+- More Ops for `convert_nchw_to_nhwc` option: LogSoftmax, ReduceMax, SplitV, Softmax
+- New optimization options in `one-optimize`: `replace_non_const_fc_with_bmm`, `resolve_customop_splitv`, `fold_densify`
+- Improved reshape elimination in `convert_nchw_to_nhwc` option.
+- Support fusion of Channel-wise Add + Relu with TConv
+- Support negative axis in ArgMin/Max
+- Show errors for unrecognized options in `one-optimize`
+- Fix shape inference for `StridedSlice`
+- Fix FuseBatchNormWithTConvPass to support TConv with bias
+- Deprecate `--O1` option in `circle2circle`
+- Support gcc-11
+- Support limited Float16 for kernels constants with dequantization to Float32
+
+## ONE Runtime
+
+### Basic Multimodel nnpackage
+- Runtime supports to run nnpackage with two models
+
+### Channel Wise Quantization on Conv2D and Depthwise Conv2D
+- Conv2D and Depthwise Conv2D supports per-channel quantization of uint8 type. 
+
+### Batch Execution with TRIX backend
+- TRIX backend supports batch execution which run in parallel with multicore
+