BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), a pioneer in edge AI on-chip processing and learning, has announced the availability of its Akida™ advanced neural networking processor in the M.2 form factor. This compact, low-power, and high-speed solution enables developers to build their own edge AI boxes, expanding the adoption of neuromorphic AI in edge computing. The M.2 form factor, around the size of a stick of gum and with a power budget of about 1 watt, is ideal for space- and power-constrained edge computing applications.

BrainChip's neural processor AI IP is an event-based technology that is inherently lower power compared to conventional neural network accelerators. The AKD1000-powered boards can be plugged into the M.2 slot, unlocking capabilities for a wide array of edge AI applications where space and power are limited, and speed is critical. These applications include industrial, factory service centers, network access devices, and more.

The AKD1000 product is available in both B+M Key and E Key configurations of the M.2 2260 form factor. It can be purchased integrated into stand-alone Raspberry PI or Edge AI box enclosures, or for integration into custom designed products. Pricing starts at $249, making it an affordable solution for developers looking to create AI solutions with Akida IP.

BrainChip's Akida processor in the M.2 form factor benefits specific use cases where space, power, and speed are critical. For instance, in industrial applications, the Akida processor's low power consumption and high-speed inference make it ideal for predictive maintenance by analyzing sensor data in real-time. This helps in identifying potential failures, reducing machine downtime, and increasing industrial efficiency and profitability. The M.2 form factor allows for easy integration into existing systems, making it a cost-effective solution for industrial AI applications.



In network access devices, the Akida processor's ability to support incremental learning and high-speed inference in low power budgets makes it suitable for tasks such as intrusion detection, anomaly detection, and cybersecurity. The M.2 form factor enables easy integration into network access devices, providing a low-cost, high-speed, and low-power consumption option for edge AI processing. This helps in improving network security and performance by enabling real-time analysis and response to potential threats.



The low power consumption of Akida significantly impacts the total cost of ownership (TCO) for edge AI devices. Lower power consumption means lower energy bills, increased battery life in battery-powered devices, reduced cooling requirements, enhanced reliability, and cost savings in SWaP-constrained applications. For example, Intellisense Systems Inc. selected Akida for its Neuromorphic Enhanced Cognitive Radio (NECR) device to enable autonomous space operations on SWaP-constrained platforms. By integrating Akida, Intellisense can provide customers with an unparalleled level of performance, adaptability, and reliability, while also reducing the overall cost of the system.

In conclusion, BrainChip's Akida processor in the M.2 form factor is a game-changer for edge computing, enabling wider adoption of neuromorphic AI in space- and power-constrained applications. The low power consumption, high-speed inference, and cost-effectiveness of Akida make it an attractive choice for developers looking to create AI solutions for industrial, network access, and other edge computing applications. As the demand for edge AI continues to grow, BrainChip's innovative solution is well-positioned to capture a significant share of the market.