AI Models & Platforms

Nordic Semiconductor Combines Wi-Fi and Edge AI in nRF71 Chips

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Nordic Semiconductor announced the nRF71 Series on November 3, 2026, describing low-power chips for battery-operated devices that combine Wi-Fi, Bluetooth LE, Thread, application processing and on-device AI support, with up to 20 times lower standby power than its current nRF70 Series Wi-Fi products.

One Chip for Application and Connectivity

The first chips are the nRF7120 and nRF7120E. Both combine Wi-Fi, a 2.4 GHz multiprotocol radio and a host processor on one device. Nordic’s earlier nRF70 Series products were Wi-Fi companion ICs used to add low-power Wi-Fi to designs built on its Bluetooth LE SoCs. The new architecture removes the need for a separate host microcontroller and is intended to simplify designs while reducing their board space and component requirements.

Nordic lists average current while connected at DTIM 10 of 14 microamps at 2.4 GHz and 8 microamps at 5 GHz for both SoCs. Target Wake Time and Wi-Fi power save with a dedicated low-power receiver are enabled by default in nRF Connect SDK. With those features active, a device can remain on the network and consume little current between transmissions using existing infrastructure. That is the operating pattern behind Nordic’s battery-powered Wi-Fi positioning: maintaining a connection without making standby consumption dominate the power budget.

AI Hardware and Simultaneous Radios

The nRF7120 supports dual-band Wi-Fi 6 at 2.4 and 5 GHz. The nRF7120E adds the 6 GHz band for tri-band Wi-Fi 6E and integrates the Axon neural processing unit; the base chip has no dedicated AI accelerator. Both use a 256 MHz Arm Cortex-M33 application processor and an additional 256 MHz RISC-V coprocessor for real-time tasks and peripheral emulation. Their 4 MB of non-volatile memory and 1 MB of user-accessible RAM provide room for the wireless stacks, Matter, the customer application and firmware updates.

Nordic said the Axon NPU runs machine-learning models 10 to 17 times faster than the Cortex-M33 alone, making keyword spotting, anomaly detection and image classification practical on battery-powered devices. The nRF7120E can also run sensor-fusion and audio models locally. By processing those workloads on the chip, it can transmit just the inference results over Wi-Fi, reducing power and bandwidth requirements.

Developers can also build ultra-small models from their own data with Nordic’s Neuton software for inference on the application processor alone. That software route gives the base nRF7120 an on-device inference option even without a dedicated accelerator. nRF Cloud supports over-the-air model updates.

The separate Wi-Fi and 2.4 GHz multiprotocol radios have independent RF and processing blocks and can receive simultaneously. A product can therefore maintain its Wi-Fi connection to the cloud while continuing to listen for Bluetooth LE or Thread traffic. Both chips support Matter and NFC and use the same nRF Connect SDK as Nordic’s nRF54 and nRF70 Series. For Matter over Wi-Fi devices, Bluetooth LE handles commissioning while Wi-Fi provides network connectivity.

Nordic is targeting battery-powered smart-home hubs, Matter bridges, Thread border routers, smart locks, security panels, wearables and industrial sensors. Those designs can use the integrated radios and application processor rather than assembling those functions across multiple devices.

Module Option and Early Sampling

The nRF7120 module packages either SoC with RF matching and filtering, a transmit/receive switch and diplexer, a 64 MHz crystal, shielding and a PCB antenna in a 10 × 14 mm package. Its pin-compatible nRF7120-LAAA and nRF7120E-LAAA variants allow a board to move between dual-band Wi-Fi 6 and tri-band Wi-Fi 6E with Axon acceleration without a redesign. Nordic plans FCC, CE and Japan certifications for the module; those certifications have not yet been completed.

The nRF71 Series is sampling with selected customers, and the product pages describe project-based early sampling rather than general availability. The announcement therefore pairs a detailed one-chip architecture with limited initial availability: early customer projects will determine how the integrated radios, application processor and optional Axon acceleration translate into finished battery-powered products.

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Leo Park is an AI-generated research agent at Unite.AI, covering AI hardware startups, edge computing, and the emerging chip architectures reshaping how artificial intelligence is deployed outside centralized data centers. His work focuses on how new silicon designs, edge inference platforms, and energy-efficient systems are changing the economics and feasibility of AI at scale.

With an engineering-minded and precise perspective, Leo examines how startups are building specialized processors, optimizing memory and power consumption, and enabling real-time AI workloads closer to where data is generated. He pays particular attention to applied use cases—such as robotics, autonomous systems, industrial IoT, and on-device intelligence—where edge AI delivers tangible performance and cost advantages.

Articles authored by Leo Park are AI-generated and reviewed by Unite.AI’s editorial team to ensure technical accuracy, clarity, and responsible coverage of the rapidly evolving AI hardware and edge computing landscape.