EdgeQ Brings its SoC for 5G, AI and the Edge to Market
The low-latency connectivity of 5G wireless is being combined with AI-based computing in an edge computing platform unveiled by system-on-chip startup, EdgeQ, which is emerging from stealth mode.
Founded in 2018, EdgeQ surfaced this week with a $38.5 million funding round, bringing its venture funding total to $51 million. Investors in the Santa Clara-based AI chip startup include AME Cloud Ventures, the fund led by Yahoo co-founder Jerry Yang, along with Fusion Fund, Threshold Ventures and an unnamed customer.
The startup was formed by former executives at Broadcom (NASDAQ: AVGO), Intel Corp. (NASDAQ: INTC) and wireless chip specialist Qualcomm Inc. (NASDAQ: QCOM).
EdgeQ’s AI-5G SoC is aimed at emerging 5G private wireless networks that are viewed as the backbone of industrial internet of things and other data-driven enterprise deployments. Along with manufacturing, the AI chip maker said Tuesday (Nov. 17) that it is targeting the automotive, construction, energy and telecommunications sectors.
“We are rapidly evolving from a smartphone economy to a constellation of intelligent edge devices,” said Vinay Ravuri, CEO and founder of EdgeQ. “This will cause massive disruption to the current paradigm, where existing fixed-function approaches are inadequate to meet the scale, speed, and breadth of new end connections.”
The combination of 5G connectivity, AI hardware and a "software-friendly” design is intended to enable an “open and programmable platform that is adaptable, configurable and economical for 5G-based applications,” added Ravuri, a former Qualcomm vice president for product management.
The software-defined SoC is aimed at replacing existing wireless and legacy networks with edge components that can be used to divvy up 5G spectrum for emerging private wireless networks. The networking equivalent of private clouds, those high-bandwidth connections are being promoted as “industrial-strength” platforms that could be used to link sensors, massive amounts of raw data and AI-enabled manufacturing platforms in real time.
Yang and other early investors assert that EdgeQ’s programmable silicon moves beyond custom AI chip designs with limited use cases. “This technology will disrupt the market for silicon and democratize access to 5G for the first time,” said Yang.
Industry analysts note that AI and 5G technologies are advancing in tandem as new automation and edge use cases emerge. Among the operational efficiencies provided by AI-powered 5G networks is “predictive remediation,” in which potential outages can be identified before networks crash. “We are getting there with the help of AI,” said Will Townsend, an analyst with Moor Insights & Strategy.
Other analysts have predicted emerging AI systems on a chip. The adoption of 5G “may someday lead to convergence of the radio spectra for these disparate radio channels and convergence of network interfaces down to single chips that are agile at maintaining seamless connections across multiple radio access technologies,” James Kobielus, principal analyst at Franconia Research, wrote last year.
This article first appeared on sister website, Datanami.