Ultra-low power AI specialist BrainChip is collaborating with ultra-low power wireless specialist HaiLa Technologies. The two companies are planning to deliver smarter, ultra-low-power solutions for intelligent connected edge devices. They aim to make it easier to run AI at the edge without draining battery life.

HaiLa’s BSC2000 is a Wi-Fi-compatible connectivity RFIC designed to showcase extreme power savings in IoT environments. The companies are planning to demonstrate how BrainChip’s Akida neuromorphic technology pairs seamlessly with HaiLa’s BSC2000 radio frequency integrated circuit RFIC.

According to the two companies, their combined technologies produce an ultra-efficient architecture that paves the way for continuously connected battery-operated devices that can last the entire life of the product on a single coin cell battery. This joint demonstration employs HaiLa’s hyper power-efficient passive backscatter wireless communication over standard Wi-Fi infrastructure with BrainChip’s Akida AKD1500 event-based AI processor. The integration provides a platform for anomaly detection, condition monitoring, and other sensor-intelligence tasks while operating on just microwatts of power.

“As a pioneer in neuromorphic computing, we are excited to partner with HaiLa to demonstrate how advanced low-power AI processing can work in tandem with ultra-efficient wireless connectivity,” said Steve Brightfield, CMO at BrainChip. “By combining our Akida technology with HaiLa’s innovative RF platform, we’re making intelligent, battery-powered edge sensors a practical reality.”

“Our collaboration with BrainChip brings together two power-conscious technologies that redefine what is possible at the edge,” said Patricia Bower, Vice President of Product Management at HaiLa. “With backscatter Wi-Fi and neuromorphic AI operating on microwatts, developers can create continuously monitored, intelligent sensors that last for years without battery replacement. This is transformative for anomaly detection, predictive maintenance, and other real-time sensing applications.”