The tech world's obsession with artificial intelligence is driving companies to develop better, more optimized solutions for running machine learning and deep learning algorithms. The latest chips are not only making AI more available to various industries, they are also driving better efficiency and increased accuracy. When it comes to artificial intelligence (AI), 2018 is looking to be a year of significant growth. This is largely due to big steps being made in machine learning and deep learning. The deep learning market alone is expected to be worth US$1.7 billion by 2022, growing at a compound annual growth rate (CAGR) of 65.3 percent during the forecast period 2016 and 2022, according to a report by market research firm MarketsandMarkets. The report cites the major factors driving growth as the robust R&D for the development of better processing hardware and increasing adoption of cloud-based technology for deep learning.

During the Windows Developer Day event yesterday, Microsoft revealed the Windows AI platform which will allow developers to build intelligent apps on Windows 10. One of the highlighted features of WinML APIs is the support for pre-trained machine learning models. Windows ML will efficiently use hardware for any given artificial intelligence (AI) workload and intelligently distributes work across multiple hardware types including CPU, GPU and Intel's Vision Processing Units (VPU). The Intel VPU is a purpose-built chip for accelerating AI workloads on client devices. Myriad X is world's first system-on-chip (SOC) shipping with a dedicated Neural Compute Engine for accelerating deep learning inferences at the edge.

With the unveiling of the Neural Compute Engine in Intel's new Movidius Myriad X VPU, Intel has stated they are one step closer to bringing true visual intelligence in drones, robotics, cameras, wearables and smart home solutions to the fore. The dedicated Neural Compute Engine is designed to give machines the ability to see, understand and react to their environments in real time. This concept of high-performance, low-power artificial intelligence is set to improve the future capabilities of devices. The video below explains more about the device capabilities: Myriad X's tiny form factor and on-board processing have been designed for autonomous devices. The main features include a proprietary Neural Compute Engine, which is a dedicated hardware block designed for accelerating neural network inferences with more than 1 TOPS of performance.

In recent years we've seen a pretty incredible explosion in CV (computer vision) enabled devices, and the software that powers them. It's not uncommon to see autonomous robots with multiple high definition cameras using sophisticated CV algorithms for guidance, even on low-cost hobbyist robots. But, that capability comes at a pretty big processing expense. Those HD videos contain a lot of pixels, and even with techniques for optimization that make processing the video more efficient, it still takes a lot processing power to do all of the complex analysis we want our robots to do. To try try and jump ahead of these increasing demands, Intel has just announced Movidius Myriad X, which is the first vision processing unit (VPU) that has a dedicated Neural Compute Engine built into the chip.

Throughout my career, and now more than ever at Intel, I have dreamed about where technology will take us next, and it's even more exciting to be creating the future. Today, that future is here with the unveiling of the Myriad X, the world's first vision processing unit (VPU) to ship with a dedicated Neural Compute Engine to deliver artificial intelligence (AI) capabilities to the edge in an incredibly low-power, high-performance package. In the coming years, we'll see a huge range of new products emerge that are made more autonomous by embedding real-time intelligence capabilities in devices – from drones and smart cameras to augmented reality and more – to give them the ability to see, understand, interact with and learn from rapidly changing environments. Myriad X combines dedicated imaging, computer vision processing and – thanks to the industry-first Neural Compute Engine – high-performance deep learning inference within the same chip, and the results are opening up new realms of possibility. With this faster, more pervasive intelligence embedded directly into devices, the potential to make our world safer, more productive and more personal is limitless.