Sub-Millisecond
Inference Latency
<1W
Continuous
Inference Power
20+
Waveform &
Modulation Classes
Zero
Cloud Dependency
RF Classification at the Edge
Without the Power Penalty
The Challenge
Existing RF Classifiers Are Too Power-Hungry for SWaP-Constrained Environment
- GPU and FPGA systems deliver high accuracy in the lab, but their power draw (typically >50W) and physical footprint disqualify them from UAV payloads and man-portable platforms — where real-time classification matters most.
- Cloud-reliant inference adds latency, exposes sensitive RF data, and fails in denied or disconnected environments.
- The result: massive false alarms, wasted resources, and operators who can’t trust their own systems.
The Solution
Akida Communication Reference Platform Brings Ultra-Low SWaP-C RF Intelligence On-Device, in Any Environment
- The Akida Communication Reference Platform pairs Akida neuromorphic AI with an SDR front end to classify 20+ waveform and modulation types on device at <1W power draw — no cloud, no network.
- Complete signal sovereignty — no raw RF data leaves the device and no exposure to interception or data exfiltration risk.
- The result: real-time RF classification on battery-powered systems; local signal intelligence without cloud dependency.
Built for Classification
Designed
for the Field

Built for Classification
Designed for the Field

RF Intelligence
Across Critical Sectors

Electronic Warfare & Threat Detection
Identify jamming waveforms, hostile emitters, and spectrum interference in real time for autonomous countermeasure decisions in real-time.

UAV / Drone Passive SIGINT
Always-on RF classification at sub-watt power, viable on small UAS platforms with no data streamed off-device.

Spectrum Monitoring & Interference Detection
Continuous waveform monitoring in congested RF environments to detect unauthorized transmissions and interference sources 24/7.

Smart Radios & Adaptive Communications
Enable cognitive radio systems to sense and adapt to spectrum conditions in real time at power levels no GPU-based solution can sustain.












