Tengine, developed by OPEN AI LAB, is a lite, high-performance, and modular inference engine for embedded device.

Tengine is composed of six modules: core/operator/serializer/executor/driver/wrapper.

• core provides the basic components and functionalities of the system.
• operator defines the schema of operators, such as convolution, relu, pooling, etc. al. Here is the current support operator list.
• serializer is to load the saved model. The serializer framework is extensible to support different format, including the customized one. caffe/onnx/Tensorflow and MXNet models can be loaded directly by Tengine.
• executor implements the code to run graph and operators. Current version provides a highly optimized implementation for multi A72 cores.
• driver is the adapter of real H/W and provides service to device executor by HAL API. It is possible for single driver to create multiple devices.
• wrapper provides the wrapper of APIs for different frameworks. Both Caffe API wrapper and Tensorflow API wrapper work now.

This version can load and run Caffe/MXNet model of mobilenet and squeezenet directly. For more details, please goto install.

NOTE: Old Caffe model has to be upgraded using upgrade_net_proto_binary/upgrade_net_proto_binary from Caffe's package.

The data is collected on 1.8G A72 and on chip RK3399, by repeating calling the forward interface to get the average time cost (ms) per run.

• Single A72 core (1xA72)

• Two A72 cores (2xA72)

For details to run benchmark, please visit benchmark page.

please refer to the install page.

please visit exmaples for applications on classification/detection.

It is easy to add new operator to Tengine. Here is the guide on new operator.

Tengine can be extended to support new serialization format, by building new serializer module.

How to build new serializer module

• Github issues
• QQ group: 829565581 (Question:Tengine Answer:openailab)
• Wechat group: add account gaojinwei01 as friend. Input your Github account name, and then you will be recognized and invited into the group

Support Tengine model file. protobuf is optional now.

Please refer to tengine_model exmaples

New features

Support GPU: using ACL (Arm computing library) as a backend graph device

Support blas operator implementation: Tengine can run on x86 without caffe now

Support new NN: Inception-v3/vgg16/faster-rcnn/ssd/yolo-v2

Support Android build: includes 32bit and 64bit

Support cross-compile on x86 (experimental): debian example contributed by mcharleb and Mani-Sadhasivam @ Linaro

Support Tensorflow serializer: load inception-v3 and mobilenet TF model directly

Support Tensorflow wrapper: label_image.cpp from tensorflow repo

Others

Single so file now and remove the etc/config according to feedback from field.

Tengine will automatically probe the CPU arch/part settings, and there is just one CPU driver now.

To assign cpu manually when necessary:

 export TENGINE_CPU_LIST=1,2 

Besides probing CPU, a few CPUs are defined in cpu_predefined.cpp, including rk3399/a63/kirin960/apq8096. To use the predefined CPU, refers to below:

const struct cpu_info * p_info=get_predefined_cpu("rk3399");
create_cpu_device("rk3399",p_info);

Introduce the driver/device model to support MT(Multi-Thread)

Support new NN: Inception-v4

Caffe Wrapper examples: squeezenet/mobilenet/mtcnn

MXNet model load examples: squeezenet/mobilenet

Support new operator: Eltwise, PReLU, Slice

Support new NN: mtcnn, resnet and lighten_cnn

Experimental caffe API wrapper: caffe based application just needs to recompile to use Tengine

Update documents, as well a few fixes.

Initial release of single A72 support