In Visual search is seen as the next great search frontier, and Microsoft’s Bing has tapped the power of NVIDIA GPUs to make it a reality. At the same time, they’ve leveraged the NVIDIA CUDA profiling toolchain and cuDNN to make the system more cost-effective.
But visual search at scale is no easy matter: Instantly delivering pertinent results when users mouse over objects within photos requires massive computations by algorithms trained to classify, detect, and match the images within images. It’s also well worth the effort.
Before now, however, it was a lengthy wait for what you were looking for. In 2015, Bing introduced image-search capabilities that enabled users to draw boxes around sub-images or click on boxes of sub-images already detected by the platform; they could then use those images as the basis of a new search.
Bing sought a solution that was fast enough to keep up with user expectations. They transitioned their object detection platform from CPUs to Azure NV-series virtual machines running NVIDIA Tesla M60 GPU accelerators.
Download the new white paper from Nvidia to explore how Bing deployed NVIDIA technology to speed up object detection and deliver pertinent results in real time.
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