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AI Edge Hardware Startup Deep Vision Raises $35 M In Series B

AI Edge Hardware Startup Deep Vision Raises $35 M In Series B

Deep Vision, Inc. has announced it received $35 million in an oversubscribed Series B financing round, led by Tiger Global. The funding proceeds will help Deep Vision expand the capabilities of its AI processor and software tools and support its rapidly growing customer base.

Tiger Global is joined by Series A investors Exfinity Venture Partners, SiliconMotion, and Western Digital, rejoining in the latest financing round.

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“We will now be able to significantly fortify our efforts to continue designing and building the world’s most power- and price-efficient AI inference platform as well as flawless software development tools”, said Ravi Annavajjhala, Deep Vision’s Chief Executive Officer. 

“We are excited to partner with Deep Vision. We believe that the company is positioned for a long runway ahead, with a unique AI processor that combines innovative software and silicon architecture for edge computing,” said Scott Shleifer, Partner, Tiger Global.

Chinnu Senthilkumar, General Partner & CTO, Exfinity Venture Partners, said, “One of the Exfinity Venture Partners’ core funding theses is to back companies leveraging skill and scale in the fertile Indo-US tech corridor, particularly in the deep tech domain from early stages”. 

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Deep Vision’s patented AI processor, named ARA-1, delivers a ground-breaking combination of performance, power, and price for camera-based applications like smart retail, driver-monitoring systems, smart city, drones, and factory automation. Although best known for its ability to perform real-time video analytics, the company’s processor also provides natural language processing (NLP) capabilities for a growing market of voice-controlled applications.

Deep Vision complements its processing technology with an extremely comprehensive, flexible, and robust set of development tools that allow its customers to easily convert their neural network models into highly optimized computation graphs ready to be deployed on the ARA-1 chip.

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