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This helps to integrate in machine learning workloads with graphics work, which makes bringing ML models to games more efficient and it gives game developers more control over synchronization on the hardware. Plus get our best Csgo-gambling coupons in our email newsletter. Models can learn to determine the best color for each pixel to benefit small images that are upscaled, or images that are zoomed in on. People have been speculating on how to make computers think more like humans for a long time and emulating the brain seems like an obvious first step. This works, not just for gaming scenarios, but in any situation where you would want to use machine learning on your local machine.

What is Microsoft providing to Game Developers? How does it work?

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By providing an ML-ready game engine, Unity has ensured that developers can start making their games more intelligent with minimal overhead. Models can learn to determine the best color for each pixel to benefit small images that are upscaled, or images that are zoomed in on. You may have had the experience when playing a game where objects look great from afar, but when you move close to a wall or hide behind a crate, things start to look a bit blocky or fuzzy — with ML we may see the end of those types of experiences.

ML Super Sampling left and bilinear upsampling right. Now that we've established the benefits of neural networks for games, let's talk about what we've developed here at Microsoft to enable games to provide the best experiences with the latest technology. A developer would choose a framework, such as CNTK, Caffe2, or Tensorflow, to build and train a model that does anything from visually improving the game to controlling NPCs.

Once they've done this, they can pipe it up to their game and expect it to run on a gamer's Windows 10 machine with no additional work on the gamer's part. This works, not just for gaming scenarios, but in any situation where you would want to use machine learning on your local machine.

We know that performance is a gamer's top priority. DirectML was built with the same principles of DirectX technology: To understand why how DirectML fits within our portfolio of graphics technology, it helps to understand what the Machine Learning stack looks like and how it overlaps with graphics.

When executed, this HLSL code receives all the benefits of running in parallel on the GPU, making inference run extremely efficiently, just like a graphics application. In DirectX, games use graphics and compute queues to schedule each frame rendered.

Because ML work is considered compute work, it is run on the compute queue alongside all the scheduled game work on the graphics queue.

When a model performs inference, the work is done in D3D12 on compute queues. DirectML efficiently records command lists that can be processed asynchronously with your game. Command lists contain machine learning code with instructions to process neurons and are submitted to the GPU through the command queue.

This helps to integrate in machine learning workloads with graphics work, which makes bringing ML models to games more efficient and it gives game developers more control over synchronization on the hardware. As mentioned previously, the principles of DirectX drive us to provide gamers and developers with the fastest technology possible.

We are confident that when vendors implement the operators themselves vs using our HLSL shaders , they will get better performance for two reasons: Knowledge of cache sizes and SIMD lanes, plus more control over scheduling are a few examples of the types of advantages vendors have when writing metacommands.

Unleashing hardware that is typically not utilized by D3D12 to benefit machine learning helps prove out incredible performance boosts. One result of this collaboration is a demo to showcase the power of metacommands. Some networks do not depend on the level of precision that FP32 offers, so by doing math in FP16, we can process around twice the amount of data in the same amount of time.

Since models benefit from this data format, the official release of WinML will support floating point 16 FP16 , which improves performance drastically. This way, you can see how long each operator takes and where it is scheduled. In addition, you can add up all the GPU time in the roll up window in order to understand how long the network is taking overall.

Microsoft and Unity share a goal of democratizing AI for gaming and game development. Once this ships, Unity games running on Windows 10 platforms will have access to inference across all hardware and the hardware acceleration that comes with DirectML.

This, combined with the convenience of using an ML-ready engine, will make getting started with Machine Learning in gaming easier than ever before. They will get all the benefit of hardware breadth and hardware acceleration with HLSL implementations of operators. The benefits of metacommands will be coming soon as we release more features of DirectML.

If you're attending GDC, check out the talks we are giving below. If not, stay tuned to the DirectX blog for more updates and resources on how to get started after our sessions. Gamers can simply keep up to date with the latest version of Windows and they will start to see new features in games and applications on Windows as they are released.

For more instructions on how to get started, please check out the forums on DirectXTech. If you're a game developer and attending GDC on Thursday, March 22nd, please attend our talks to get a practical technical deep dive of what we're offering to developers. We will be co-presenting with NV on our work to bring Machine Learning to games. Using Artificial Intelligence to Enhance your Game 1 of 2 This talk will be focused on how we address how to get started with WinML and the breadth of hardware it covers.

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