Enhancing Microsoft Bing Visual Search with NVIDIA’s Accelerated Libraries
By Extreme Investor Network – October 10, 2024
Microsoft Bing Visual Search, a powerful tool that allows users to search using images, has recently received a significant performance boost through a collaboration with NVIDIA. By incorporating NVIDIA’s TensorRT, CV-CUDA, and nvImageCodec into Bing’s TuringMM visual embedding model, the search engine has achieved a remarkable 5.13x increase in speed for offline indexing pipelines, ultimately enhancing efficiency and reducing costs.
The Power of Multimodal AI and Visual Search
Multimodal AI technologies, like Microsoft’s TuringMM, play a crucial role in applications that require seamless interaction between different data types, such as text and images. These advanced models, like CLIP, enable tasks such as text-based visual search, zero-shot image classification, and image captioning, providing users with enhanced search capabilities and improved accuracy.
Optimization Through NVIDIA’s Technologies
The optimization of Bing’s visual embedding pipeline was made possible by leveraging NVIDIA’s GPU acceleration technologies. By using TensorRT for model execution, the efficiency of computationally expensive layers in transformer architectures was improved, leading to a significant reduction in latency for image processing tasks. Additionally, the utilization of nvImageCodec and CV-CUDA accelerated image decoding and preprocessing stages, further enhancing performance.
Implementation and Outstanding Results
Prior to optimization, Bing’s visual embedding model operated on a GPU server cluster, handling various deep learning services across Microsoft. By integrating NVIDIA’s libraries, the throughput of the pipeline increased from 88 queries per second to 452 queries per second, showcasing a remarkable 5.14x speedup. Not only did these enhancements improve processing speed, but they also offloaded tasks from CPUs to GPUs, maximizing power efficiency.
Driving Future Innovations in Visual Search
The successful collaboration between Microsoft and NVIDIA demonstrates the immense potential of accelerated libraries in enhancing AI-driven applications. By effectively utilizing GPU resources, deep learning and image processing workloads can be accelerated, even in systems that already employ GPU acceleration. These advancements pave the way for more efficient and responsive visual search capabilities, benefiting both users and service providers.
For a more in-depth look at the optimization process, visit the original NVIDIA Technical Blog.
Image source: Shutterstock
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