Texas Instruments powers edge AI with TinyEngine innovation from its GCC

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Texas Instruments strengthens edge AI with TinyEngine developed through its GCC
Texas Instruments strengthens edge AI with TinyEngine developed through its GCC

As artificial intelligence moves beyond cloud data centres, Texas Instruments is advancing edge AI with TinyEngine, a Neural Processing Unit (NPU) developed through its India Global Capability Centre (GCC). The innovation is designed to help AI applications run directly on edge devices with greater speed and lower power consumption.

Edge devices process data where it is generated instead of sending it to cloud servers. These include wearable health trackers, industrial sensors, robots, smart home appliances, medical devices and automotive systems. Running AI on such devices has remained challenging because traditional processors often lack the performance and energy efficiency required for real-time processing.

To address this, engineers at Texas Instruments India developed TinyEngine, an NPU integrated into the company’s microcontrollers. These low-power chips are designed to perform dedicated functions while consuming minimal computing resources. The new NPU enables AI models to operate more efficiently, reducing processing delays and lowering power consumption on edge devices.

“TI India employees are embedded in the company’s global operating model, collaborating closely with TI’s global technology and R&D teams and customers to drive innovation. Our teams in India contribute to the entire chip design process, from concept to design, product engineering, testing and validation, and system software,” Santhosh Kumar, President and Managing Director of Texas Instruments India, said.

According to the company, TinyEngine can run AI models up to 90 times faster while consuming more than 120 times less energy per inference compared to similar microcontrollers without a dedicated AI accelerator.

The development reflects Texas Instruments’ expanding research and development capabilities in India, where engineering teams have contributed significantly to the company’s embedded processing and AI technologies.

Alongside the hardware, Texas Instruments has also developed a software ecosystem to simplify AI deployment on edge devices. The combined platform is intended to help developers build AI-powered solutions across industries such as industrial automation, healthcare and consumer electronics.

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