Pros of MNN

• More extensive platform support, including iOS, Android, Windows, and Linux
• Better performance optimization, especially on mobile devices
• More active development and frequent updates

Cons of MNN

• Steeper learning curve due to more complex architecture
• Less focus on embedded systems compared to Tengine
• Larger binary size, which may be a concern for some embedded applications

Code Comparison

MNN example:

auto input = _Input({1, 3, 224, 224}, NC4HW4);
auto conv = _Conv(3, 16, {3, 3}, VALID);
auto output = conv(input);

Tengine example:

struct tensor* input = get_graph_input_tensor(graph, 0, 0);
struct tensor* conv = create_tensor(graph, "conv", TENGINE_DT_FP32);
struct node* conv_node = create_node(graph, "conv", "CONV");

Both libraries offer C/C++ APIs for model deployment, but MNN's API tends to be more high-level and object-oriented, while Tengine's API is more C-style and lower-level. MNN generally provides more abstraction and ease of use for complex models, while Tengine offers finer control over low-level operations, which can be beneficial for embedded systems.

Pros of ncnn

• Wider platform support, including mobile and embedded devices
• More optimized for mobile and edge computing scenarios
• Larger community and more frequent updates

Cons of ncnn

• Steeper learning curve due to more complex API
• Less focus on high-level abstractions, requiring more low-level implementation

Code Comparison

ncnn:

ncnn::Net net;
net.load_param("model.param");
net.load_model("model.bin");

ncnn::Mat in = ncnn::Mat::from_pixels(image_data, ncnn::Mat::PIXEL_BGR, w, h);
ncnn::Mat out;
net.extract("output", out);

Tengine:

graph_t graph = create_graph(NULL, "tengine", model_file);
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
set_tensor_shape(input_tensor, dims, 4);
set_tensor_buffer(input_tensor, input_data, input_size);
run_graph(graph, 1);

Both libraries offer efficient inference for deep learning models, but ncnn is more focused on mobile and embedded scenarios, while Tengine provides a simpler API with a focus on ease of use. ncnn has a larger community and more frequent updates, but may require more low-level implementation. Tengine offers a more straightforward approach but with potentially less optimization for specific platforms.

Pros of mace

• More extensive documentation and examples
• Better support for mobile platforms, especially Android
• Active community and regular updates

Cons of mace

• Steeper learning curve for beginners
• Limited support for some less common neural network operations

Code comparison

mace:

MaceEngine mace_engine(device_type);
mace_engine.Init(net_def, input_nodes, output_nodes, device_context);
mace_engine.Run(inputs, &outputs);

Tengine:

graph_t graph = create_graph(NULL, "tengine", model_file);
prerun_graph(graph);
run_graph(graph, 1);

Summary

mace offers better mobile support and documentation, while Tengine provides a simpler API and easier integration for embedded systems. mace's code is more object-oriented, while Tengine uses a C-style API. Both projects aim to optimize deep learning models for edge devices, but cater to slightly different use cases and developer preferences.

Pros of ComputeLibrary

• Extensive support for ARM-based architectures, optimized for ARM CPUs and GPUs
• Comprehensive documentation and examples for various use cases
• Active development and regular updates from ARM

Cons of ComputeLibrary

• Limited cross-platform support compared to Tengine
• Steeper learning curve for developers not familiar with ARM architectures
• Larger codebase and potentially higher resource requirements

Code Comparison

Tengine example (inference):

graph_t graph = create_graph(NULL, "tengine", model_file);
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
set_tensor_shape(input_tensor, dims, 4);
prerun_graph(graph);
run_graph(graph, 1);

ComputeLibrary example (convolution):

NEConvolutionLayer conv;
conv.configure(&src, &weights, &biases, &dst, conv_info, weights_info);
NEScheduler::get().schedule(&conv, Window::DimY);

Pros of TVM

• Broader ecosystem support with multiple frontends (e.g., PyTorch, TensorFlow, ONNX)
• More extensive documentation and community resources
• Advanced optimization techniques, including AutoTVM and AutoScheduler

Cons of TVM

• Steeper learning curve due to its complexity and extensive features
• Potentially higher resource requirements for compilation and optimization

Code Comparison

TVM example (tensor addition):

import tvm
from tvm import te

n = te.var("n")
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")

Tengine example (tensor addition):

struct tensor* A = get_graph_tensor(graph, "input1");
struct tensor* B = get_graph_tensor(graph, "input2");
struct tensor* C = get_graph_tensor(graph, "output");
float* a_data = (float*)get_tensor_buffer(A);
float* b_data = (float*)get_tensor_buffer(B);
float* c_data = (float*)get_tensor_buffer(C);

Both TVM and Tengine aim to optimize deep learning models for various hardware platforms. TVM offers a more comprehensive solution with advanced features and broader ecosystem support, while Tengine focuses on lightweight deployment for embedded systems. TVM's code tends to be more high-level and abstract, whereas Tengine's code is closer to traditional C programming.

Pros of TNN

• More extensive model support, including popular architectures like ResNet, MobileNet, and YOLO
• Better optimization for mobile and embedded devices, with specific support for ARM and OpenCL
• More active development and frequent updates

Cons of TNN

• Steeper learning curve due to more complex architecture
• Less focus on lightweight deployment for IoT devices
• Potentially higher resource requirements for some use cases

Code Comparison

TNN example (C++):

auto net = std::make_shared<TNN::TNN>();
TNN::Status status = net->Init(proto, model);
auto instance = net->CreateInst(network_config, status);
instance->Forward();

Tengine example (C):

graph_t graph = create_graph(NULL, "tengine", model_file);
prerun_graph(graph);
run_graph(graph, 1);

Summary

TNN offers more extensive model support and optimization for mobile devices, while Tengine focuses on lightweight deployment for IoT. TNN has a more complex architecture but provides better performance on mobile platforms. Tengine is simpler to use and more suitable for resource-constrained environments. The code examples demonstrate that TNN uses a C++ interface with object-oriented design, while Tengine employs a C-style API with a more procedural approach.