AlphaICs has raised $8 million in funding for artificial intelligence processors for edge computing.
The round was led by Emerald Technology Ventures and Endiya Partners.
The Milpitas, California-based company was started in 2016 by Vinod Dham, a former Intel executive known as the “father of the Pentium.” He teamed up with younger chip designers to develop Real AI processors for edge applications, those at the edge of the network. Dham is now a board member, and the company is run by CEO Pradeep Vajram.
The team is creating a coprocessor chip that can do agent-based artificial intelligence. These RAP chips could one day be deployed in computing devices and autonomous cars to make decisions at lightning speeds, or in datacenters on a massive scale.
Above: Edge computing is getting more popular.
Image Credit: AlphaICs
The company will use the funds to finish designing its Gluon AI chip, develop the software stack, and build system solutions for its target markets.
The round included existing investors ReBright Partners and 3One4 Capital, along with Aaruha Technology Fund, Ireon Ventures, Canal Ventures, JSR Corporation, CBC Co., and Whiteboard Capital.
Emerald investment director Michal Natora will join AlphaICs’ board of directors.
With the growth in popularity of deep neural networks, there has been a huge demand for running such networks on edge devices in real time. Market researcher Omdia forecasts that global AI edge chipset revenue will grow from $7.7 billion in 2019 to $51.9 billion by 2025 at a compound annual growth rate (CAGR) of 37.5%.
AlphaICs’ Real AI Processor
Above: Real AI Processors can handle edge tasks such as identifying objects on the road.
Image Credit: AlphaICs
AlphaICs’ chips are based on a proprietary highly modular and scalable architecture, enabling AI acceleration for low-power edge applications, as well as high-performance edge datacenters. The chips do a lot of processing on-chip, rather than reaching out remotely for processing in datacenters.
AlphaICs’ architecture provides inference performance and is equally suited for edge learning. The rapidly developing field of edge learning promotes privacy, enables automated labeling, and facilitates continuous learning of new scenarios.
The applications include markets such as high-end smartphones, wearables, and enterprise markets like robots, cameras, and sensors. Varjam said the company is working with strategic partners to bring products out in the industrial, automotive, and surveillance markets. The company has 19 employees.
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