Compiling from source, cuDNN version is not compatible? How can I change the cuDNN compile version?

I use mvn install java-0.5.0 in jetson with below environment. Finally it completed and generated the "tensorflow-core-api-0.5.0-linux-arm64-gpu.jar".

System information

• OS Platform and Distribution : Linux Ubuntu 20.04 arm64
• TensorFlow installed from (source or binary): source
• TensorFlow version: 2.10.1
• Java version (i.e., the output of java -version): openjdk version "11.0.20"
• Java command line flags (e.g., GC parameters):
• Installed from Maven Central?:
• Bazel version (if compiling from source): 5.4.1
• GCC/Compiler version (if compiling from source): 9.4.0
• CUDA/cuDNN version: CUDA11.4+CuDNN8.6.0
• GPU model and memory:

But when I run the model , the error is occured. Here is the error log:
"2023-08-01 09:57:03.793190: E external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_dnn.cc:377] Loaded runtime CuDNN library: 8.6.0 but source was compiled with: 8.9.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration."

I have tried upgrade CuDNN to 8.9.0 and run again, but it's run failed with error-"tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at conv_ops.cc:1143 : NOT_FOUND: No algorithm worked! Error messages:". I think this may be the environment doesn't match.

So how can I resolve this problem? I think this is a way to fix the problem that is to change the cuDNN compile version, but I can't find any info about this.
P.S.: I run the model successfully in my local machine(windows11 x86_64...)

Best Regards

Beginne damit, den Build und die Modellausführung von mvn install java-0.5.0 in der gemeldeten Jetson-Umgebung zu reproduzieren und dabei die während der Kompilierung verwendete cuDNN-Version mit der Laufzeitversion zu vergleichen. Als erledigt gilt die Aufgabe, wenn das generierte GPU-JAR das Modell erfolgreich mit kompatiblen CUDA- und cuDNN-Versionen ausführt oder die erforderliche Kompilierungskonfiguration dokumentiert ist.