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Deep Vision, a company developing an AI accelerator chip and software suite for edge computing workloads, today announced that it closed a $35 million series B financing round led by Tiger Global, with participation from Exfinity Venture Partners, Silicon Motion, and Western Digital. According to CEO Ravi Annavajjhala, the proceeds will be put toward product development as Deep Vision looks to ramp up manufacturing of its hardware for early customers.
Edge computing, which puts computing and storage resources at the location where data is produced, is thriving as industries including manufacturing, health care, and energy increasingly embrace it. Grand View Research predicts that the global edge computing market will reach $6.29 billion in value in 2021, up from $4.68 billion in 2020.
Palo Alto, California-based Deep Vision, which sprung out of research that founders Rehan Hameed and Wajahat Qadeer conducted at Stanford, is among the many companies developing specialized processors for AI edge applications. Targeting use cases like retail, driver-monitoring systems, smart city installations, drones, and factory automation, the startup’s ARA-1 flagship chip can perform real-time video analytics as well as natural language processing for voice-controlled apps.
“This investment is a resounding affirmation of Deep Vision’s tactical accomplishments and strategic direction, which are rapidly driving our company into a wide variety of applications in our key target markets,” Annavajjhala, who previously worked at Intel and SanDisk, said in a press release. “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.”
Deep Vision offers software alongside the ARA-1 that optimizes AI models to run on the accelerator chip. It essentially converts models into computation graphs — graphs with equation data — ready to be deployed on the ARA-1.
Deep Vision has rivals in Sima.ai, AIStorm, Hailo, Quadric, and Flex Logix, which are similarly developing chips customized for AI workloads. Mobileye, the Tel Aviv company Intel acquired for $15.3 billion in March 2017, also offers a computer vision processing solution for AVs in its EyeQ product line. Baidu last July unveiled Kunlun, a chip for edge computing on devices and in the cloud via datacenters. And Arm debuted two new AI edge computing chips in February.
But according to Linley Group principal analyst Linley Gwennap, Deep Vision’s products are differentiated by their performance, accuracy, and resolution. For example, the ARA-1’s architecture is designed to keep data movement at a minimum, reducing latency.
“To improve latency and reliability for voice and other cloud services, edge products such as drones, security cameras, robots, and smart retail applications are implementing complex and robust neural networks. We expect 1.9 billion edge devices to ship with deep learning accelerators in 2025,” Gwennap said. “Within these edge AI applications, we see an increasing demand for more performance, greater accuracy, and higher resolution. This fast-growing market provides a large opportunity for Deep Vision’s AI accelerator, which offers impressive performance and low power.”
Deep Vision says it’s actively sampling its chips and has “several” customers so far. To date, the company has raised $54 million in venture capital.
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