Achieving High-Performance Vision Processing in IoT
Advancements in machine learning are enabling an expanding and more capable IoT ecosystem, equipping devices such as connected cameras, robots, drones and smart home solutions with improved on-device vision processing and analytics capabilities.
A hybrid approach combining deep learning with traditional computer vision can deliver significant performance and power-efficiency improvements for IoT applications requiring vision processing. In this webinar you will learn:
- Benefits and trade-offs between traditional computer vision and machine learning for IoT applications using vision processing
- Use cases and benefits of on-device vision processing
- How to implement a hybrid approach combining computer vision and deep learning for superior vision processing performance and power-efficiency
- Concrete results from hybrid computer vision + deep learning implementations
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Shardul Brahmbhatt, Product Manager, Staff, Qualcomm Technologies
Shardul Brahmbhatt leads software and platforms product management for IoT at Qualcomm Technologies. In this role, Shardul is driving adoption of On-Device AI across IoT segments and manages collaborations for Intelligent Edge-Cloud as well as On-Device AI implementation on Qualcomm IoT Platforms. He is currently involved in creating reference platforms and software for Smart Home/Appliances, Smart Speaker, Connected Camera and Robotics segments. He was also the lead for the Wearables and Smart Industrial IoT Software and Platforms product strategy and before moving to his present role, Shardul led the engineering team to enable Windows 10 on Snapdragon at Qualcomm Technologies.
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