As an Associate Member, Kalray was proud to represent the Khronos Group during the keynote sessions at Autosens 2019, held in Brussels, Belgium, from Sept.17 to 19, 2019. Stephane Strahm, Senior Product Manager at Kalray, talked about "Open minds to Open Standards for the deep learning automotive solutions". In order to efficiently orchestrate the adoption of the automotive industry's rapidly advancing connected technology, it is always mandatory in evaluating standards or creating standards, to improve interoperability of components as systems' complexity grows. This is happening nowhere more so than in the area of intelligent data compute for tactical driving systems – autonomous vehicles. Khronos' open standards are a key solution to providing versatility in the supply chain and embracing more of the collective AI development community to solve tomorrow's goals.

Several neural network frameworks for deep learning exist, all of which offer distance features and functionality. Transferring neural networks between frameworks, however, creates extra time and work for developers. The Khronos Group, an open consortium of leading hardware and software companies creating advanced acceleration standards, has developed NNEF (Neural Network Exchange Format), an open, royalty-free standard that allows hardware manufacturers to reliably exchange trained neural networks between training frameworks and inference engines. Neural networks are trained using a variety of different frameworks and are then deployed on a similarly-wide variety of inference engines, each of which has its own proprietary format. This diversity is highly desirable but is also where the problem lies.

Machine learning is advancing at a booming pace, both in the smart devices we interact with daily, and in commercial and industrial sectors of the economy. Machine learning capabilities are being added to everything from social media platforms, internet of things (IoT) devices and cameras to robots and cars. But the pace of innovation is leading to fragmentation, and one potential consequence of that fragmentation is a risk of stalling. Fragmentation is a common problem affecting many industries that either lack standards or are inundated by many competing standards. It can especially plague emerging technologies -- ABI Research reports that fragmentation has affected the virtual reality (VR) industry -- and it can be tricky to judge when and how to standardize without stifling innovation.