update README.md for Antares Next

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+## What is Antares:
+
+**Antares** (https://github.com/microsoft/antares) is an engine to auto generate optimized kernels for [Multi Backends](backends). It is a framework not only for ***Software developers*** to get backend-related code, but also for ***Hardware developers*** to extend new backends/hareware quickly and easily. Antares frontend is based on [Antares IR](AntaresIR.md) that follows "One Language Syntax for All Platforms".
+
+### How to Install:
+
+```sh
+python3 -m pip install --upgrade antares
+```
+
+### Quick Test:
+
+```sh
+BACKEND=c-scpu antares
+
+# List Supported Backends
+antares backends
+
+# Help Information:
+antares help
+```
+
+### Usage Examples (antares save/eval/compile):
+
+```sh
+# Quickly generate a multi-threaded CPU code:
+BACKEND=c-mcpu antares
+
+# Search an efficient multi-threaded CPU code and save best code to specified location:
+STEP=100 BACKEND=c-mcpu antares save ./kernel_example.cpp
+
+# Reproduce kernel evaluation based on an early saved source code:
+BACKEND=c-mcpu antares eval ./kernel_example.cpp
+
+# Freeze kernels and compiled into edge-side binaries:
+BACKEND=c-mcpu antares compile ./kernel_example.cpp ./output-dest/
+# Build solution in destination directory:
+cd ./output-dest && make
+```
+
+### Advanced Examples:
+```sh
+# Quickly generate a CUDA code with correctness checking:
+CHECK=1 BACKEND=c-cuda antares
+
+# Search an efficient multi-threaded CPU code showing progress bar only:
+PROGRESS=1 STEP=100 BACKEND=c-mcpu antares save ./kernel_example.cpp
+
+# Quickly generate a SHADER code for Windows 10/11's DirectX12:
+BACKEND=c-hlsl_win64 antares
+
+# Quickly generate an ROCm code for AMDGPU (requires ROCm SDK >= 4.2):
+BACKEND=c-rocm antares
+
+# Quickly generate a CUDA code for computing MatMul (512,512)x(512,512) based on [Antares IR](AntaresIR.md) for NVIDIA GPU (requires NVIDIA CUDA SDK >= 10.0):
+BACKEND=c-cuda COMPUTE_V1='- S = 512; einstein_v2(input_dict={"input0": {"dtype": "float32", "shape": [S, S]}, "input1": {"dtype": "float32", "shape": [S, S]}}, exprss="output0[N, M] +=! input0[N, K] * input1[K, M]")' antares
+
+# Search an efficient CUDA code for MatMul, using 2000 steps for trial:
+BACKEND=c-cuda STEP=2000 COMPUTE_V1='- S = 512; einstein_v2(input_dict={"input0": {"dtype": "float32", "shape": [S, S]}, "input1": {"dtype": "float32", "shape": [S, S]}}, exprss="output0[N, M] +=! input0[N, K] * input1[K, M]")' antares
+
+# Cleanup history caches:
+antares clean
+
+```
+
+## Contributing
+
+This project welcomes contributions and suggestions.  Most contributions require you to agree to a
+Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine whether you need to provide
+a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+
+## Trademarks
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
+trademarks or logos is subject to and must follow 
+[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
+Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
+Any use of third-party trademarks or logos are subject to those third-party's policies.
+