Autonomous Driving and Deep Learning - Solutions for Machine Vision?

An autonomously driving car using DL methods is creating a lot of rumor. Heterogenous CPU architectures - not only with GPU accelerators - are necessary. Is this suitable for machine vision, too? Of course, DL methods have the potential to solve machine vision applications, too. But does the sector have the army of engineers working in the automotive industry? No. Take a look at easy architectures and libraries. While the learning phase of DL needs huge (cloud-based) computing power, the application is much easier. Implemented on typical multi-core ARM or i-Core-based computer systems, DL systems can be realized. Programming, debugging - also important to be on the market in time. IMAGO's ne

GPUs in the VisionBox

IMAGO integrates long-time available GPU cards into the VisionBox – and this in a compact form factor. Equipped with an Core-i processor under Windows Embedded / IoT or Linux OS, the “VisionBox Serval+” and “VisionBox Panther” are provided with a half-sized GPU card and thus supply more computing power for applications like hyperspectral image processing or 3D. Additionally, IMAGO advises their customers concerning SW optimizations of C++ or OpenCL code on this or alternative Embedded Vision platforms. Not every algorithm runs generally fast on a GPU – expert knowledge is therefore recommended. #Pressrelease #MachineVision

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