Pros of XNNPACK

• Broader platform support, including x86, ARM, and WebAssembly
• More extensive operator coverage, including convolutions and pooling
• Active development and frequent updates

Cons of XNNPACK

• Potentially more complex integration due to its broader scope
• May have a larger footprint for simple use cases

Code Comparison

QNNPACK (C++):

qnnp_status qnnp_initialize(void);
qnnp_status qnnp_deinitialize(void);

XNNPACK (C):

enum xnn_status xnn_initialize(const struct xnn_allocator* allocator);
enum xnn_status xnn_deinitialize(void);

Key Differences

• QNNPACK focuses on quantized neural network operations, while XNNPACK covers a wider range of neural network primitives.
• XNNPACK offers more flexibility in terms of supported architectures and platforms.
• QNNPACK is more tightly integrated with PyTorch, while XNNPACK is designed as a standalone library.
• XNNPACK provides more granular control over memory allocation through its initialization function.

Both libraries aim to optimize neural network operations on mobile and embedded devices, but XNNPACK has a broader scope and more active development. QNNPACK may be easier to use for PyTorch-specific projects, while XNNPACK offers more versatility for cross-platform development.

Pros of TensorFlow

• Larger ecosystem with more tools, libraries, and community support
• Better support for production deployment and serving models
• More comprehensive documentation and learning resources

Cons of TensorFlow

• Steeper learning curve and more complex API
• Slower development cycle and less flexibility for research

Code Comparison

QNNPACK (PyTorch):

qnnp_operator_t convolution_op = nullptr;
qnnp_create_convolution2d_nhwc_q8(
    padding_top, padding_right, padding_bottom, padding_left,
    kernel_height, kernel_width,
    stride_height, stride_width,
    dilation_height, dilation_width,
    groups, group_input_channels, group_output_channels,
    input_zero_point, kernel_zero_point, kernel,
    bias, output_zero_point, output_min, output_max,
    0, &convolution_op);

TensorFlow:

conv = tf.keras.layers.Conv2D(
    filters=32,
    kernel_size=(3, 3),
    strides=(1, 1),
    padding='valid',
    activation='relu',
    input_shape=(28, 28, 1)
)

QNNPACK is a low-level quantized neural network library, while TensorFlow provides a higher-level API for building and training neural networks. QNNPACK focuses on efficient inference on mobile and embedded devices, whereas TensorFlow offers a more comprehensive framework for both training and inference across various platforms.

Pros of MNN

• Broader platform support, including mobile and IoT devices
• More comprehensive feature set, including model conversion and optimization tools
• Active development and frequent updates

Cons of MNN

• Steeper learning curve due to more complex architecture
• Less integration with PyTorch ecosystem
• Potentially slower performance for specific use cases

Code Comparison

MNN example:

auto input = _Input({1, 3, 224, 224}, NC4HW4);
auto conv = _Conv(0.0f, 0.0f, input, {32, 3, 3, 3}, VALID);
auto output = _Convert(conv, NCHW);

QNNPACK example:

qnnp_operator_t convolution = nullptr;
qnnp_create_convolution2d_nhwc_q8(
    padding_top, padding_right, padding_bottom, padding_left,
    kernel_height, kernel_width,
    stride_height, stride_width,
    dilation_height, dilation_width,
    groups, group_input_channels, group_output_channels,
    input_zero_point, input_scale,
    kernel_zero_point, kernel_scale,
    kernel, bias,
    output_zero_point, output_scale,
    output_min, output_max,
    0, &convolution);

Both libraries offer efficient implementations for neural network operations, but MNN provides a higher-level API and more extensive tooling, while QNNPACK focuses on low-level optimizations for quantized operations, particularly within the PyTorch ecosystem.

Pros of ncnn

• Broader platform support, including mobile and embedded devices
• More comprehensive set of pre-trained models and operators
• Easier to use for deployment in production environments

Cons of ncnn

• Less integration with PyTorch ecosystem
• May have slightly lower performance for some specific operations
• Documentation can be less detailed compared to QNNPACK

Code Comparison

ncnn example:

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

ncnn::Mat in(224, 224, 3);
ncnn::Mat out;
net.input("data", in);
net.extract("output", out);

QNNPACK example:

torch::jit::script::Module module = torch::jit::load("model.pt");
module.eval();

torch::Tensor input = torch::rand({1, 3, 224, 224});
torch::Tensor output = module.forward({input}).toTensor();

Both libraries aim to optimize neural network computations, but ncnn focuses more on deployment across various platforms, while QNNPACK is specifically designed for quantized operations within the PyTorch framework.

Pros of MACE

• Supports a wider range of mobile platforms, including Android, iOS, and Linux
• Provides a complete end-to-end solution for deploying deep learning models on mobile devices
• Offers built-in model conversion tools for various frameworks like TensorFlow and Caffe

Cons of MACE

• Less focused on quantization compared to QNNPACK
• May have a steeper learning curve due to its broader scope and features
• Potentially larger overhead for simple use cases

Code Comparison

MACE example (model deployment):

#include "mace/public/mace.h"

MaceEngine engine;
MaceStatus status = CreateMaceEngineFromProto(model_graph_proto,
                                              model_graph_proto_size,
                                              model_weights_data,
                                              model_weights_data_size,
                                              input_names,
                                              output_names,
                                              device_type,
                                              &engine);

QNNPACK example (operator usage):

#include <qnnpack.h>

qnnp_initialize();
qnnp_operator_t convolution;
qnnp_create_convolution2d_nhwc_q8(
    padding_top, padding_right, padding_bottom, padding_left,
    kernel_height, kernel_width,
    stride_height, stride_width,
    dilation_height, dilation_width,
    groups, group_input_channels, group_output_channels,
    input_zero_point, input_scale,
    kernel_zero_point, kernel_scale,
    kernel, bias,
    output_zero_point, output_scale,
    output_min, output_max,
    0, &convolution);

Pros of TNN

• Broader platform support, including mobile and embedded devices
• More comprehensive model optimization techniques
• Extensive support for various neural network architectures

Cons of TNN

• Less integration with PyTorch ecosystem
• Potentially steeper learning curve for developers familiar with PyTorch

Code Comparison

QNNPACK example (C++):

qnnp_operator_t convolution;
qnnp_create_convolution2d_nhwc_q8(
    padding_top, padding_right, padding_bottom, padding_left,
    kernel_height, kernel_width,
    stride_height, stride_width,
    dilation_height, dilation_width,
    groups, group_input_channels, group_output_channels,
    input_zero_point, input_scale,
    kernel_zero_point, kernel_scale,
    kernel, bias,
    output_zero_point, output_scale,
    output_min, output_max,
    flags,
    &convolution);

TNN example (C++):

auto conv_layer = std::make_shared<ConvLayerAcc>();
conv_layer->Init(context_, param_, resource_, inputs, outputs);
conv_layer->Forward(inputs, outputs);

The code examples show that QNNPACK focuses on quantized operations with detailed parameter configuration, while TNN provides a higher-level abstraction for layer implementation and execution.