System on a Chip (SoC) start-up EdgeQ (Cupertino, CA) announced its launch from stealth with $51 million in total funding, including $38.5 million in a Series A round. Backed by investors Threshold Ventures (formerly DFJ), Fusion Fund, Yahoo! co-founder Jerry Yang (AME Cloud Ventures) and an unannounced strategic customer, EdgeQ will address the 5G infrastructure market with products aimed at delivering 5G connectivity with AI computing.
The company counts experience in cellular modem development from Qualcomm, Intel and Broadcom on its team. It sees a limited number of players in the market, focused especially on smartphones, leaving room for new providers targeting edge devices and infrastructure.
EdgeQ said it will deliver a converged 5G and AI silicon platform that is open and software programmable for both devices and edge infrastructure. By introducing open programmability to the baseband, EdgeQ wants to provides a new software-driven development model for OEMs and operators, supporting existing cellular protocols such as 4G and 5G as well as the next generation of networks.
EdgeQ’s AI-5G SoC is aimed at emerging 5G private 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) 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.”
“We provide an open platform converging 5G plus AI, which abstracts much of the complexities for our customers working on 5G deployment—from supporting multiple chipsets, different software stacks, board design, cost, power, and latencies in transferring data in between, not to mention, potential security hazards. Though we are not ready to disclose the hardware details, our 5G chip architecture uniquely lends itself to AI in a way without needing an extra AI accelerator hardware, saving both power and cost to the end customer.”
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.
Ravuri said Qualcomm’s 5G SoC design (targeted at 5G endpoints) was closed while EdgeQ’s was open. “Their chip technology does not support 5G connectivity and AI computing, making it inadequate for enterprise-grade 5G infrastructure, which needs robust computing capabilities in addition to 5G,” he said. “We can bring the best of breed here—the cellular, but also offer to the market what they’re really looking for, which is an open ecosystem where they are able to innovate and add/develop features on this chipset that they can’t do otherwise. That is what we see as a big departure from the existing Qualcomm offerings.”
The software-defined SoC is aimed at replacing existing wireless and legacy networks with edge components that can be used to divide and partition 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, research director at Futurum Research, wrote last year.