BrainChip AKD1500 Now Available in Compact M.2 Form Factor, Enabling Fanless Edge AI in Industrial and Commercial Designs

Key Terms

neuromorphic processor technical

A neuromorphic processor is a specialized computer chip built to work more like a brain than a traditional processor: it uses many simple units that communicate locally and respond to events, which lets it recognize patterns and make decisions with much lower power and latency. For investors, this matters because such chips can enable energy-efficient artificial intelligence in devices from phones and cameras to industrial sensors, creating new product opportunities, cost savings, and competitive advantages in markets where power, speed, or on-device privacy are important.

m.2 form factor technical

A M.2 form factor is a small, rectangular slot and card standard used inside computers and devices to add components like solid-state drives, Wi‑Fi modules, or cellular modems. Think of it as a postage‑stamp sized expansion card that plugs directly into a motherboard and can use different connection types (e.g., SATA or PCIe/NVMe) for varying speed and capability. Investors care because M.2 affects device size, performance, and upgradeability, which influence demand for storage and component makers as products shift toward thinner, faster designs.

edge ai accelerator technical

A hardware chip or module designed to run artificial intelligence tasks directly on devices at the network “edge” (phones, sensors, cameras, industrial controllers) rather than in remote data centers. Like a compact engine tuned for specific AI workloads, an edge AI accelerator speeds up model inference while using less power and reducing data sent over networks, which affects device performance, deployment cost, and the size of markets for edge computing products.

on-chip learning technical

On-chip learning is the ability of a computer chip to adjust how it processes information by updating its own internal settings without sending data to a separate computer. Think of it as a small appliance that can tune itself while running instead of sending parts to a repair shop. For investors, this matters because chips that learn on-device can reduce energy use, lower latency, protect data privacy, and open new product markets, potentially improving a maker’s competitiveness and margins.

See more from StockTitan in Google Search and AI answers. Adds StockTitan as a preferred source · opens Google

Add on Google Not now

Lower-cost, lower-power edge AI accelerator gives designers a plug-and-play path to upgrade legacy systems to Akida

LAGUNA HILLS, Calif.--(BUSINESS WIRE)--

AKD1500 with M.2 board

The new AKD1500 M.2 module is positioned as a low-cost, ultra-low-power edge AI accelerator available for plug-and-play deployment in the smallest M.2 standard socket, giving hardware designers a drop-in path to add on-device AI to legacy systems without redesigning the power supply or cooling solution.

Cutting Power and Cost Without Cutting Corners

The design approach enables fanless operation in portable and stationary designs. It supports small-form-factor designs for on-device AI without a complete thermal and power redesign.

“Power and price have been the two biggest barriers keeping AI out of fanless industrial equipment and battery-powered commercial devices,” said Steve Brightfield, chief product officer at BrainChip. “With AKD1500 in the M.2 form factor, engineering teams can add capable, on-device AI to an existing design without touching the power supply or the cooling solution. That's the difference between a multi-quarter redesign and a drop-in upgrade.”

Industry Backdrop

The launch comes as industrial and commercial edge AI adoption accelerates. Manufacturing is projected to generate nearly $25 billion in edge AI chipset revenue by 2031, the largest of any market vertical, according to ABI Research's 2Q 2026 edge AI chipset market update. This is driven in part by the need for specialized, power-efficient silicon in complex, device-dense industrial environments where average selling prices run higher than in consumer categories.

ABI also notes that edge AI's ultra-low-latency capabilities are becoming essential to scale industrial applications such as robotics, workplace safety monitoring and preventative maintenance. The researcher also finds that growing industrial IoT deployment is driving demand for low-power, high-performance semiconductor solutions the AKD1500 M.2 module is designed to deliver.

Smallest Form Factor, Broadest Reach

AKD1500's M.2 availability reinforces BrainChip's positioning as the efficient AI solution that can be easily added to a legacy system, extending neuromorphic, on-chip learning to industrial and commercial designs that cannot accommodate the power draw, heat or cost of conventional edge AI accelerators.

AKD1500 ships in the smallest M.2 2230 (22x30 mm) form factor with a B+M key edge connector, making it suitable for a neuromorphic accelerator and freeing up M.2 slots typically reserved for memory and modems. This allows tablets and other portable, connectivity-dependent devices to be upgraded with on-device AI without sacrificing storage or wireless connectivity, a common trade-off with larger accelerator modules.

Availability

The AKD1500 M.2 module is available now through BrainChip's web store.

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

BrainChip is the worldwide leader in neuromorphic 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 and processing data with unmatched efficiency, precision, and energy economy. BrainChip's Temporal Event-based Neural Networks (TENNs) build on State-Space Models (SSMs) with time-sensitive, event-driven frameworks that are ideal for real-time streaming applications. These innovations make low-power Edge AI deployable across industries such as aerospace, autonomous vehicles, robotics, industrial IoT, consumer devices, and wearables. BrainChip is advancing the future of intelligent computing, bringing AI closer to the sensor and closer to real-time.

Explore more at www.brainchip.com. Follow BrainChip on Twitter or LinkedIn.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260728144608/en/

Media Contact:
Madeline Coe
PRforBrainchip@bospar.com

Source: BrainChip Holdings Ltd.