BrainChip Announces Commercial Availability and Production Shipments of AKD1500 Neuromorphic Processors

Production quantities are currently shipping to customers as the chip undergoes comprehensive industrial and military-grade qualification.

LAGUNA HILLS, Calif. – June 30, 2026 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, event-based neuromorphic AI technology, today announced the commercial availability and initial production shipments of its Akida™ AKD1500 reference chips.

Manufactured in partnership with GlobalFoundries (GF) using their advanced 22nm Fully Depleted Silicon-on-Insulator (22FDX®) technology, the production-ready AKD1500 delivers highly efficient, sub-watt continuous inferencing natively at the digital edge.

The AKD1500 acts as an efficient, standalone or hardware co-processor, avoiding the wasteful always-on energy consumption of traditional AI accelerators by only processing data when events occur. By delivering near-terabyte operations-per-second (TOPS) scale efficiency while consuming less than 300mW in PCIe mode and under 200mW in serial mode, the AKD1500 removes the traditional power and thermal boundaries of edge deployments.

Production Shipments and Extreme Environmental Qualification

Multiple BrainChip customers have begun receiving production-volume quantities of the AKD1500 for use in both defense and wearable use cases. To support deployment across the most demanding physical environments, the device will undergo rigorous qualification testing for an array of extreme thermal and environmental screening tiers as required by customers.

The AKD1500 is being qualified across two distinct delivery formats to accommodate diverse hardware engineering requirements: packaged silicon, comprising standard form-factor packages engineered for high-reliability circuit boards and bare die for direct-to-substrate silicon integration for highly space-constrained or custom multi-chip modules.

These dual configurations and screening protocols enable sovereign, cloud-independent AI execution under severe shock, vibration and temperature conditions, directly addressing critical industrial IoT, automotive, aerospace and defense applications.

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Flexible Dual-Interface System Architecture

To ensure frictionless ecosystem integration, the AKD1500 has been engineered with a versatile interconnect architecture that supports both high-performance and ultra-low-power host systems. This includes a PCIe Interface for complex edge architectures, allowing AKD1500 to operate as a high-speed offload engine alongside robust x86, ARM or RISC-V application processors and serial interfaces optimized for low-end, battery constrained embedded systems. The interfaces bring advanced pattern recognition and local learning to low-cost microcontrollers without requiring a system redesign.

“The commercial availability of the AKD1500 production chip marks a profound milestone in our commercialization roadmap,” said Sean Hehir, CEO at BrainChip. “By delivering true, sub-watt neuromorphic compute in both packaged and die formats, we are providing industrial and defense partners with the rugged hardware flexibility they need to deploy intelligence anywhere — completely free from cloud dependence.”

Key Specifications Summary

About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)

BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, Akida, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. In enabling effective edge compute to be universally deployable across real-world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future.