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Mindgrove Technologies, an India-based semiconductor design company, has announced plans to commercially launch its second System on Chip (SoC), named V2600, by late 2026. The chip is purpose-built for CCTV and surveillance applications where end-to-end security is a critical requirement. It supports edge AI, i.e. AI-based video processing and decision making on the SoC itself.
This new chip is the first of a family of chips designed for various vision applications, including secure CCTV, automotive dashcams and driver-assistance systems, machine vision for industrial automation, multi-input biometric access control, smart city surveillance, smart TVs and connected consumer devices. By enabling fast, on-device processing of visual data, the chip supports real-time decision-making at the edge, thus significantly reducing reliance on cloud infrastructure. "Vision systems are rapidly becoming foundational to security, mobility, and industrial automation, leading to an explosion in demand. Simultaneously, businesses are increasingly conscious of where the tech comes from and how sensitive data is handled," said Shashwath T R, Co-founder and CEO of Mindgrove Technologies. He added, "With the MG Vision series of SoCs, we are responding to the growing global demand for trusted alternatives in surveillance and vision-led systems. Security is built directly into the silicon, ensuring visual data remains protected from capture through processing." The announcement comes at a time of heightened regulatory scrutiny of connected surveillance and video infrastructure, alongside a broader global focus on data sovereignty, supply-chain resilience, and system integrity. Designed for deployments in regulated and high-trust environments, MG V2600 is well-suited for national infrastructure, enterprise security, and large-scale commercial applications. Commenting on the technology behind the chip, Sharan Srinivas J, Co-founder and CTO of Mindgrove Technologies, said, "The MG Vision SoC series has been architected to handle complex vision workloads directly at the edge, where latency, reliability, and data security are critical. By tightly integrating compute, vision processing, and hardware-level security, we enable developers to build high-performance vision applications without exposing sensitive data to the cloud. This not only improves system responsiveness but also materially strengthens end-to-end security." (ANI)
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