Australia-based technology company BrainChip has launched a new hardware platform designed to classify radio frequency (RF) signals in real time while operating on less than a watt of power, targeting defense and intelligence applications that require processing at the edge.

Called the Akida Communication Reference Platform, the system is built around the company’s Akida AKD1500 neuromorphic processor and is intended for defense contractors, government agencies, software-defined radio vendors and edge AI developers.

The platform performs RF signal classification directly on-device, eliminating the need for cloud connectivity or large computing hardware. BrainChip said the system is designed for environments where power availability, thermal constraints and size limitations make conventional GPU- or FPGA-based solutions difficult to deploy.

Potential applications include handheld signal intelligence systems, unmanned aerial vehicles, satellite communications terminals and other portable defense technologies.

AI tackles RF threats

According to BrainChip, the platform can identify more than 20 signal modulation types in real time while maintaining greater than 85% accuracy at a signal-to-noise ratio of 30 decibels.

Traditional digital signal processing systems typically rely on rule-based approaches that can struggle to identify new or intentionally modified signals. BrainChip says its neuromorphic AI approach allows the system to capture unknown waveforms and use them for future model retraining, enabling adaptation to emerging wireless threats.

The company argues that this flexibility addresses a key limitation of existing RF classification systems, which often require significant computational resources and can be difficult to update for changing operational conditions.

The platform is also designed to support battery-powered deployments where continuous operation is required without access to external infrastructure.

“BrainChip’s Akida Communication Reference Platform proves that real-time signal intelligence can be condensed into a portable battery powered solution to extend the range of deployment options,” said Sean Hehir, BrainChip’s CEO.

Built for edge deployment

The platform is available for evaluation and partner integration as a reference design kit. It supports integration with software-defined radio front ends such as the USRP B205mini and EPIQ Sidekiq.

A host system, including devices like the Raspberry Pi 5, can be used to manage data flow and application development. BrainChip said engineering teams can use the platform to prototype intelligence, surveillance and reconnaissance capabilities, as well as broader signal intelligence applications.

The launch expands the company’s portfolio of edge AI reference platforms beyond radar and sensor-fusion systems. BrainChip says the new offering demonstrates how neuromorphic computing can be applied to RF processing workloads that traditionally require larger and more power-hungry hardware.

The release comes as defense agencies and communications providers increasingly explore AI-powered spectrum monitoring tools capable of operating in remote and contested environments. By reducing power consumption and hardware requirements, such systems could enable RF awareness capabilities in smaller platforms that previously lacked sufficient computing resources.

As military and commercial users continue pushing AI capabilities closer to sensors and data sources, low-power processors capable of running advanced inference at the edge are becoming increasingly important for real-time decision-making in disconnected environments.

Recommended Articles