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namespace arm_compute
{
/**
@page architecture Library architecture

@tableofcontents

@section S4_1_1 Core vs Runtime libraries

The Core library is a low level collection of algorithms implementations, it is designed to be embedded in existing projects and applications:

- It doesn't allocate any memory (All the memory allocations/mappings have to be handled by the caller).
- It doesn't perform any kind of multi-threading (but provide information to the caller about how the workload can be split).

The Runtime library is a very basic wrapper around the Core library which can be used for quick prototyping, it is basic in the sense that:

- It allocates images and tensors by using standard malloc().
- It multi-threads Neon code in a very basic way using a very simple pool of threads.
- For OpenCL it uses the default CLScheduler command queue for all mapping operations and kernels.

For maximum performance, it is expected that the users would re-implement an equivalent to the runtime library which suits better their needs (With a more clever multi-threading strategy, load-balancing between Neon and OpenCL, etc.)

@section S4_1_2 Data-type and Data-layout support

Compute Library supports a wide list of data-types, information can been directly found in the documentation of each kernel/function.
The main data-types that the Machine Learning functions support are the following:
- BFLOAT16: 16-bit non-standard brain floating point
- F16: 16-bit half precision floating point
- F32: 32-bit single precision floating point
- QASYMM8: 8-bit unsigned asymmetric quantized
- QASYMM8_SIGNED: 8-bit signed asymmetric quantized
- QSYMM8_PER_CHANNEL: 8-bit signed symmetric quantized (Used for the weights)

Moreover, Compute Library supports the following data layouts (fast changing dimension from right to left):
- NHWC: The native layout of Compute Library that delivers the best performance where channels are in the fastest changing dimension
- NCHW: Legacy layout where width is in the fastest changing dimension
where N = batches, C = channels, H = height, W = width

@section S4_1_3 Fast-math support

Compute Library supports different types of convolution methods, fast-math flag is only used for the Winograd algorithm.
When the fast-math flag is enabled, both Neon and CL convolution layers will try to dispatch the fastest implementation available, which may introduce a drop in accuracy as well. The different scenarios involving the fast-math flag are presented below:
- For FP32:
    - no-fast-math: Only supports Winograd 3x3,3x1,1x3,5x1,1x5,7x1,1x7
    - fast-math: Supports Winograd 3x3,3x1,1x3,5x1,1x5,7x1,1x7,5x5,7x7
- For fp16:
    - no-fast-math: No Winograd support
    - fast-math: Supports Winograd 3x3,3x1,1x3,5x1,1x5,7x1,1x7,5x5,7x7

@section S4_1_4 Thread-safety

Although the library supports multi-threading during workload dispatch, thus parallelizing the execution of the workload at multiple threads, the current runtime module implementation is not thread-safe in the sense of executing different functions from separate threads.
This lies to the fact that the provided scheduling mechanism wasn't designed with thread-safety in mind.
As it is true with the rest of the runtime library a custom scheduling mechanism can be re-implemented to account for thread-safety if needed and be injected as the library's default scheduler.

@section S4_2_windows_kernels_mt_functions Windows, kernels, multi-threading and functions

@subsection S4_2_1_windows Windows

A @ref Window represents a workload to execute, it can handle up to @ref Coordinates::num_max_dimensions dimensions.
Each dimension is defined by a start, end and step.

It can split into subwindows as long as *all* the following rules remain true for all the dimensions:

- max[n].start() <= sub[n].start() < max[n].end()
- sub[n].start() < sub[n].end() <= max[n].end()
- max[n].step() == sub[n].step()
- (sub[n].start() - max[n].start()) % max[n].step() == 0
- (sub[n].end() - sub[n].start()) % max[n].step() == 0

@subsection S4_2_2 Kernels

Each implementation of the @ref IKernel interface (base class of all the kernels in the core library) works in the same way:

OpenCL kernels:

@code{.cpp}
// Initialize the CLScheduler with the default context and default command queue
// Implicitly initializes the CLKernelLibrary to use ./cl_kernels as location for OpenCL kernels files and sets a default device for which OpenCL programs are built.
CLScheduler::get().default_init();

cl::CommandQueue q = CLScheduler::get().queue();
//Create a kernel object:
MyKernel kernel;
// Initialize the kernel with the input/output and options you want to use:
kernel.configure( input, output, option0, option1);
// Retrieve the execution window of the kernel:
const Window& max_window = kernel.window();
// Run the whole kernel in the current thread:
kernel.run( q, max_window ); // Enqueue the kernel to process the full window on the default queue

// Wait for the processing to complete:
q.finish();
@endcode

Neon / CPP kernels:

@code{.cpp}
//Create a kernel object:
MyKernel kernel;
// Initialize the kernel with the input/output and options you want to use:
kernel.configure( input, output, option0, option1);
// Retrieve the execution window of the kernel:
const Window& max_window = kernel.window();
// Run the whole kernel in the current thread:
kernel.run( max_window ); // Run the kernel on the full window
@endcode

@subsection S4_2_3 Multi-threading

The previous section shows how to run a Neon / CPP kernel in the current thread, however if your system has several CPU cores, you will probably want the kernel to use several cores. Here is how this can be done:

@code{.cpp}
    ThreadInfo info;
    info.cpu_info = &_cpu_info;

    const Window      &max_window     = kernel->window();
    const unsigned int num_iterations = max_window.num_iterations(split_dimension);
    info.num_threads                  = std::min(num_iterations, _num_threads);

    if(num_iterations == 0)
    {
        return;
    }

    if(!kernel->is_parallelisable() || info.num_threads == 1)
    {
        kernel->run(max_window, info);
    }
    else
    {
        int  t         = 0;
        auto thread_it = _threads.begin();

        for(; t < info.num_threads - 1; ++t, ++thread_it)
        {
            Window win     = max_window.split_window(split_dimension, t, info.num_threads);
            info.thread_id = t;
            thread_it->start(kernel, win, info);
        }

        // Run last part on main thread
        Window win     = max_window.split_window(split_dimension, t, info.num_threads);
        info.thread_id = t;
        kernel->run(win, info);

        try
        {
            for(auto &thread : _threads)
            {
                thread.wait();
            }
        }
        catch(const std::system_error &e)
        {
            std::cerr << "Caught system_error with code " << e.code() << " meaning " << e.what() << '\n';
        }
    }
@endcode

This is a very basic implementation which was originally used in the Neon runtime library by all the Neon functions.

@sa CPPScheduler

@note Some kernels need some local temporary buffer to perform their calculations. In order to avoid memory corruption between threads, the local buffer must be of size: ```memory_needed_per_thread * num_threads``` and a unique thread_id between 0 and num_threads must be assigned to the @ref ThreadInfo object passed to the ```run``` function.

@subsection S4_2_4 Functions

Functions will automatically allocate the temporary buffers mentioned above, and will automatically multi-thread kernels' executions using the very basic scheduler described in the previous section.

Simple functions only call a single kernel (e.g NEConvolution3x3), while more complex ones consist of several kernels pipelined together (e.g @ref NEFullyConnectedLayer ). Check their documentation to find out which kernels are used by each function.

@code{.cpp}
//Create a function object:
MyFunction function;
// Initialize the function with the input/output and options you want to use:
function.configure( input, output, option0, option1);
// Execute the function:
function.run();
@endcode

@warning The Compute Library requires Mali OpenCL DDK r8p0 or higher (OpenCL kernels are compiled using the -cl-arm-non-uniform-work-group-size flag)

@note All OpenCL functions and objects in the runtime library use the command queue associated with CLScheduler for all operations, a real implementation would be expected to use different queues for mapping operations and kernels in order to reach a better GPU utilization.

@subsection S4_4_1_cl_scheduler OpenCL Scheduler and kernel library

The Compute Library runtime uses a single command queue and context for all the operations.

The user can get / set this context and command queue through CLScheduler's interface.

The user can get / set the target GPU device through the CLScheduler's interface.

@attention Make sure the application is using the same context as the library as in OpenCL it is forbidden to share objects across contexts. This is done by calling @ref CLScheduler::init() or @ref CLScheduler::default_init() at the beginning of your application.

@attention Make sure the scheduler's target is not changed after function classes are created.

All OpenCL kernels used by the library are built and stored in @ref CLKernelLibrary.
If the library is compiled with embed_kernels=0 the application can set the path to the OpenCL kernels by calling @ref CLKernelLibrary::init(), by default the path is set to "./cl_kernels"

@subsection S4_4_2_events_sync OpenCL events and synchronization

In order to block until all the jobs in the CLScheduler's command queue are done executing the user can call @ref CLScheduler::sync() or create a sync event using @ref CLScheduler::enqueue_sync_event()

@subsection S4_4_2_cl_neon OpenCL / Neon interoperability

You can mix OpenCL and Neon kernels and functions. However it is the user's responsibility to handle the mapping/unmapping of OpenCL objects.

@section S4_5_algorithms Algorithms

All computer vision algorithms in this library have been implemented following the [OpenVX 1.1 specifications](https://www.khronos.org/registry/vx/specs/1.1/html/). Please refer to the Khronos documentation for more information.

@section S4_6_images_tensors Images, padding, border modes and tensors

Most kernels and functions in the library process images, however, in order to be future proof most of the kernels actually accept tensors. See below for more information about how they are related.

@attention Each memory object can be written by only one kernel, however it can be read by several kernels. Writing to the same object from several kernels will result in undefined behavior. The kernel writing to an object must be configured before the kernel(s) reading from it.

@subsection S4_6_1_padding_and_border Padding and border modes

Several algorithms require a neighborhood around the current pixel to compute it's value. This means the algorithm will not be able to process the borders of the image unless you give it more information about how those border pixels should be processed. The @ref BorderMode enum is used for this purpose.

You have 3 types of @ref BorderMode :

- @ref BorderMode::UNDEFINED : Neighbor pixels outside of the image are treated as undefined. As a result all the pixels which are on the border will have a value which is undefined.
- @ref BorderMode::REPLICATE : Neighbor pixels outside of the image are treated as having the same value as the closest valid pixel.
- @ref BorderMode::CONSTANT : Neighbor pixels outside of the image are treated as having the same constant value. (The user can choose what this value should be).

Moreover both OpenCL and Neon use vector loads and stores instructions to access the data in buffers, so in order to avoid having special cases to handle for the borders all the images and tensors used in this library must be padded.

@subsubsection padding Padding

There are different ways padding can be calculated:

- Accurate padding:

@note It's important to call allocate @b after the function is configured: if the image / tensor is already allocated then the function will shrink its execution window instead of increasing the padding. (See below for more details).

- Manual padding / no padding / auto padding: You can allocate your images / tensors up front (before configuring your functions). In that case the function will use whatever padding is available and will shrink its execution window if there isn't enough padding available (which translates into a smaller valid region for the output). See also @ref valid_region).
If you don't want to manually set the padding but still want to allocate your objects upfront then you can use auto_padding. It guarantees that the allocation will have enough padding to run any of the provided functions.

@code{.cpp}
Image     src, dst;

// Use auto padding for the input:
src.info()->init_auto_padding(TensorShape(640u,480u), Format::U8);

// Use manual padding for the destination image
dst.info()->init(src.info()->tensor_shape(), Format::U8, strides_in_bytes, offset_first_element_in_bytes, total_size_in_bytes);

// Allocate all the images
src.allocator()->allocate();
dst.allocator()->allocate();
// Fill the input image with the content of the PPM image if a filename was provided:
fill_image(src);

NEGaussian3x3 gauss;

// Apply a Gaussian 3x3 filter to the source image (Note: if the padding provided is not enough then the execution window and valid region of the output will be shrunk)
gauss.configure(&src, &dst, BorderMode::UNDEFINED);

//Execute the functions:
gauss.run();
@endcode

@warning Some kernels need up to 3 neighbor values to calculate the value of a given pixel. Therefore, to be safe, we use a 4-pixel padding all around the image. In addition, some kernels read and write up to 32 pixels at the same time. To cover that case as well we add an extra 32 pixels of padding at the end of each row. As a result auto padded buffers waste a lot of memory and are less cache friendly. It is therefore recommended to use accurate padding or manual padding wherever possible.

@subsubsection valid_region Valid regions

Some kernels (like edge detectors for example) need to read values of neighboring pixels to calculate the value of a given pixel, it is therefore not possible to calculate the values of the pixels on the edges.

Another case is: if a kernel processes 8 pixels per iteration and the image's dimensions are not a multiple of 8 and not enough padding is available then the kernel will not be able to process the pixels near the right edge. As a result these pixels will be left undefined.

In order to know which pixels have been calculated, each kernel sets a valid region for each output image or tensor. See also @ref TensorInfo::valid_region(), @ref ValidRegion

@subsection S4_6_2_tensors Tensors

Tensors are multi-dimensional arrays with a maximum of @ref Coordinates::num_max_dimensions dimensions.

Depending on the number of dimensions tensors can be interpreted as various objects. A scalar can be represented as a zero-dimensional tensor and a vector of numbers can be represented as an one-dimensional tensor. Further, an image is actually just a 2D tensor, a 3D tensor can be seen as an array of images and a 4D tensor as a 2D array of images, etc.

@note Most algorithms process images (i.e a 2D slice of the tensor), therefore only padding along the X and Y axes is required (2D slices can be stored contiguously in memory).

@subsection S4_6_3_description_conventions Images and Tensors description conventions

Image objects are defined by a @ref Format and dimensions expressed as [width, height, batch]

Tensors are defined by a @ref DataType plus a number of channels (Always expected to be 1 for now) and their dimensions are expressed as [width, height, feature_maps, batch].

In other words, the lower three dimensions of a tensor specify a single input in [width, height, feature_maps], while any other specified dimension represents a batch in the appropriate dimension space.
For example, a tensor with dimensions [128, 128, 64, 16] represents a 1D batch space with 16 batches of 128 elements in width and height and 64 feature maps each.
Each kernel specifies the expected layout of each of its tensors in its documentation.

@note Unless specified otherwise in the kernel's or function's documentation all tensors and images parameters passed must have identical dimensions.

@note Unless specified otherwise in the kernel's or function's documentation the number of channels for tensors is expected to be 1 (For images, the number of channels is inferred from the @ref Format).

@attention Regardless of the @ref DataType used by a tensor the @ref ITensor::buffer() method will always return a uint8_t pointer, and all the metadata in @ref TensorInfo will be expressed in bytes. It is the user's responsibility to cast the pointer to the correct type.

For example, to read the element located at the coordinates (x,y) of a float tensor:

@code{.cpp}
float value = *reinterpret_cast<float*>(input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y)));
@endcode

@subsection S4_6_4_working_with_objects Working with Images and Tensors using iterators

The library provides some iterators to access objects' data.
Iterators are created by associating a data object (An image or a tensor for example) with an iteration window.

Iteration windows are defined by an array of dimensions, each of which consists of a start, end and step.

The @ref execute_window_loop function takes an execution window, a lambda function and one or more iterators.
It will iterate through every element of the execution window and for each element it will update the iterators accordingly and call the lambda function.

Here are a couple of examples of how to use the iterators to fill / read tensors:

@snippet examples/neon_copy_objects.cpp Copy objects example

@subsection S4_6_5_sub_tensors Sub-tensors

Sub-tensors are aliases to existing Tensors, as a result creating a sub-tensor does not result in any underlying memory allocation.

Sub-tensors can be used to access a sub-set of the parent tensor, something that can be useful in case different operations need to be performed on different parts of a tensor.

Moreover, sub-tensors can be used to perform zero copy tensor concatenation.

The API for creating a sub-tensor is the following:
@code{.cpp}
SubTensor(ITensor *parent, const TensorShape &tensor_shape, const Coordinates &coords)
@endcode

Where \a parent is the parent tensor which we want to create an alias for, \a tensor_shape is the shape of the sub-tensor and \a coords are the starting indexing coordinates of the sub-tensor within the parent tensor.

@note Two sub-tensor concrete classes for different targets are currently supported : @ref CLSubTensor and @ref SubTensor

@warning Limitation of the sub-tensor is that it cannot be extracted spatially, meaning sub-tensors should have the same width and height as the parent tensor. The main reasons for this is the fact that individual kernels might need to operate with a step size that is not a multiple of the sub-tensor spatial dimension. This could lead to elements being overwritten by different kernels operating on different sub-tensors of the same underlying tensor.

@section S4_7_memory_manager MemoryManager

@ref IMemoryManager is a memory managing interface that can be used to reduce the memory requirements of a given pipeline by recycling temporary buffers.

@subsection S4_7_1_memory_manager_components MemoryGroup, MemoryPool and MemoryManager Components

@subsubsection S4_7_1_1_memory_group MemoryGroup

@ref IMemoryGroup defines the memory managing granularity.

MemoryGroup binds a number of objects to a bucket of memory requirements that need to be fulfilled in order for an operation or list of operations to be executed.

Requesting backing memory for a specific group can be done using @ref IMemoryGroup::acquire and releasing the memory back using @ref IMemoryGroup::release.

@subsubsection S4_7_1_2_memory_pool MemoryPool

@ref IMemoryPool defines a pool of memory that can be used to provide backing memory to a memory group.

@note @ref BlobMemoryPool is currently implemented which models the memory requirements as a vector of distinct memory blobs.

@subsubsection S4_7_1_2_memory_manager_components MemoryManager Components

@ref IMemoryManager consists of two components:
- @ref ILifetimeManager that keeps track of the lifetime of the registered objects of the memory groups and given an @ref IAllocator creates an appropriate memory pool that fulfils the memory requirements of all the registered memory groups.
- @ref IPoolManager that safely manages the registered memory pools.

@note @ref BlobLifetimeManager is currently implemented which models the memory requirements as a vector of distinct memory blobs.

@subsection S4_7_2_working_with_memory_manager Working with the Memory Manager
Using a memory manager to reduce the memory requirements of a pipeline can be summed in the following steps:

Initially a memory manager must be set-up:
@code{.cpp}
Allocator  allocator{};                                                               // Create an allocator to use for the backing memory allocation
auto lifetime_mgr  = std::make_shared<BlobLifetimeManager>();                         // Create Lifetime Manager
auto pool_mgr      = std::make_shared<PoolManager>();                                 // Create Pool Manager
auto mm            = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); // Create Memory Manager
@endcode

Once done, memory groups can be registered to use the memory manager:
@code{.cpp}
MemoryGroup memory_group(mm); // Create a memory group and set the memory manager to use
@endcode

@note If a memory manager is not specified then all allocation will be immediate instead of deferred through the memory manager.

Next step is to set objects to be managed by the memory group. It is important though to note that the lifetime of an object is tracked from the @ref MemoryGroup::manage() and the @ref TensorAllocator::allocate calls.
@ref MemoryGroup::manage flags that the object will be needed starting now and when @ref TensorAllocator::allocate is called it signals the end of the object lifetime.
@code{.cpp}
Tensor tmp1, tmp2, tmp3;            // Create example tensors
memory_group.manage(&tmp1);         // Start managing object tmp1 and start its lifetime
memory_group.manage(&tmp2);         // Start managing object tmp2 and start its lifetime

operation1.configure(&tmp1, &tmp2); // Configure a function/kernel using tmp1 and tmp2

tmp1.allocator()->allocate();       // Flag that the lifetime of object tmp1 has ended

memory_group.manage(&tmp3);         // Start managing object tmp3 and start its lifetime

operation2.configure(&tmp2, &tmp3); // Configure a function/kernel using tmp2 and tmp3

tmp2.allocator()->allocate();       // Flag that the lifetime of object tmp2 has ended
tmp3.allocator()->allocate();       // Flag that the lifetime of object tmp3 has ended
@endcode

@warning The configuration step should be done sequentially by a single thread so that all the lifetimes are captured correclty.

When configuration of all the operations is finished then the memory manager have to be populated:
@code{.cpp}
mm->populate(&allocator), 2 /* num_pools */); // Populate memory manager pools
@endcode

Finally, during execution of the pipeline the memory of the appropriate memory group should be requested before running:
@code{.cpp}
memory_group.acquire(); // Request memory for the group

operation1.run();       // Run operation1
operation2.run();       // Run operation2

memory_group.release(); // Release memory so that it can be reused
@endcode
@note Execution of a pipeline can be done in a multi-threading environment as memory acquisition/release are thread safe.
@note If you are handling sensitive data and it's required to zero out the memory buffers before freeing, make sure to also zero out the intermediate buffers. You can access the buffers through the memory group's mappings.

@subsection S4_7_3_memory_manager_function_support Function support

Most of the library's function have been ported to use @ref IMemoryManager for their internal temporary buffers.

If that is the case, a memory manager can be passed to them during construction to reuse memory among these functions.
@code{.cpp}
// Setup Memory Manager
CLBufferAllocator  allocator{};                                                       // Create an allocator to use for the backing memory allocation
auto lifetime_mgr  = std::make_shared<BlobLifetimeManager>();                         // Create Lifetime Manager
auto pool_mgr      = std::make_shared<PoolManager>();                                 // Create Pool Manager
auto mm            = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); // Create Memory Manager

// Create two convolution layers and use the memory manager to manager their internal temporary buffers
CLConvolutionLayer conv1(mm), conv2(mm);

// Configure layers
conv1.configure(...);
conv2.configure(...);

// Populate memory manager
mm->populate(&allocator), 1 /* num_pools */); // Populate memory manager pools

// Run layers (Memory will be recycled for internal buffers for conv1 and conv2
conv1.run();
conv2.run();
@endcode

@section S4_8_import_memory Import Memory Interface

The implemented @ref TensorAllocator and @ref CLTensorAllocator objects provide an interface capable of importing existing memory to a tensor as backing memory.

A simple Neon example can be the following:
@code{.cpp}
// External backing memory
void* external_ptr = ...;

// Create and initialize tensor
Tensor tensor;
tensor.allocator()->init(tensor_info);

// Import existing pointer as backing memory
tensor.allocator()->import_memory(external_ptr);
@endcode

It is important to note the following:
- Ownership of the backing memory is not transferred to the tensor itself.
- The tensor mustn't be memory managed.
- Padding requirements should be accounted by the client code. In other words, if padding is required by the tensor after the function configuration step, then the imported backing memory should account for it. Padding can be checked through the @ref TensorInfo::padding() interface.

@section S4_9_opencl_tuner OpenCL Tuner

OpenCL kernels when dispatched to the GPU take two arguments:
- The Global Workgroup Size (GWS): That's the number of times to run an OpenCL kernel to process all the elements we want to process.
- The Local Workgroup Size (LWS): That's the number of elements we want to run in parallel on a GPU core at a given point in time.

The LWS can be required by an algorithm (For example if it contains memory barriers or uses local memory) but it can also be used for performance reasons to tweak the performance of a kernel: the execution time of the overall kernel might vary significantly depending on how the GWS is broken down.

However, there is no universal rule regarding which LWS is best for a given kernel, so instead we created the @ref CLTuner.

When the @ref CLTuner is enabled ( Target = 2 for the graph examples), the first time an OpenCL kernel is executed the Compute Library will try to run it with a variety of LWS values and will remember which one performed best for subsequent runs. At the end of the run the @ref graph::Graph will try to save these tuning parameters to a file.

However this process takes quite a lot of time, which is why it cannot be enabled all the time. @ref CLTuner supports three modes of tuning with different trade-offs between the time taken to tune and the kernel execution time achieved using the best LWS found. In the Exhaustive mode, it searches all the supported values of LWS. This mode takes the longest time to tune and is the most likely to find the optimal LWS. Normal mode searches a subset of LWS values to yield a good approximation of the optimal LWS. It takes less time to tune than Exhaustive mode. Rapid mode takes the shortest time to tune and finds an LWS value that is at least as good or better than the default LWS value. The mode affects only the search for the optimal LWS and has no effect when the LWS value is imported from a file.

But, when the @ref CLTuner is disabled ( Target = 1 for the graph examples), the @ref graph::Graph will try to reload the file containing the tuning parameters, then for each executed kernel the Compute Library will use the fine tuned LWS if it was present in the file or use a default LWS value if it's not.

@section S4_10_weights_manager Weights Manager

@ref IWeightsManager is a weights managing interface that can be used to reduce the memory requirements of a given pipeline by reusing transformed weights across multiple function executions.
@ref IWeightsManager is responsible for managing weight tensors alongside with their transformations.
@ref ITransformWeights provides an interface for running the desired transform function. This interface is used by the weights manager.

@subsection S4_10_1_working_with_weights_manager Working with the Weights Manager
Following is a simple example that uses the weights manager:

Initially a weights manager must be set-up:
@code{.cpp}
auto  wm = std::make_shared<IWeightsManager>(); // Create a weights manager
@endcode

Once done, weights can be managed, configured and run:
@code{.cpp}
wm->manage(weights); // Manage the weights
wm->acquire(weights, &_reshape_weights_managed_function); // Acquire the address of the transformed weights based on the transform function
wm->run(weights, &_reshape_weights_managed_function);     // Run the transpose function
@endcode

@section S5_0_experimental Experimental Features

@subsection S5_1_run_time_context Run-time Context

Some of the Compute Library components are modelled as singletons thus posing limitations to supporting some use-cases and ensuring a more client-controlled API.
Thus, we are introducing an aggregate service interface @ref IRuntimeContext which will encapsulate the services that the singletons were providing and allow better control of these by the client code.
Run-time context encapsulates a list of mechanisms, some of them are: scheduling, memory management, kernel caching and others.
Consequently, this will allow finer control of these services among pipelines when Compute Library is integrated in higher level frameworks.

This feature introduces some changes to our API.
All the kernels/functions will now accept a Runtime Context object which will allow the function to use the mentioned services.

Finally, we will try to adapt our code-base progressively to use the new mechanism but will continue supporting the legacy mechanism to allow a smooth transition. Changes will apply to all our three backends: Neon, OpenCL and OpenGL ES.
*/
} // namespace arm_compute