A few weeks ago, we noted that the Brainchip AKD1500 M.2 module could work with the Raspberry Pi 5 using a compatible M.2 HAT+ expansion card to evaluate

A few weeks ago, we noted that the Brainchip AKD1500 M.2 module could work with the Raspberry Pi 5 using a compatible M.2 HAT+ expansion card to evaluate the solution or develop applications using the company’s low-power, low-latency neuromorphic AI technology. To make things even easier, they’ve now introduced the AKD1500 Raspberry Pi 5 Dev Kit that packs a Raspberry Pi 5 8GB, an official M.2 HAT+, and an Akida AKD1500 M.2 B+M-Key card into a metal case. Most ports from the Raspberry Pi 5 are exposed, namely the Gigabit Ethernet port, the four USB connectors, the two HDMI video outputs, and the USB-C port for power. The Raspberry Pi 5 power button and an LED on the M.2 HAT+ are also visible through openings in the metal case. The kit requires a camera for operation, but instead of relying on a MIPI CSI-based Raspberry Pi Camera module, users are [...] The post Brainchip AKD1500 Raspberry Pi 5 Dev Kit enables users to quickly experiment with event-based neuromorphic AI appeared first on CNX Software - Embedded Systems News.

Next-generation edge AI dev kit pairing Raspberry Pi 5 with BrainChip's AKD1500 neuromorphic co-processor for ultra-low-power inference and on-device learning at the edge.

Laguna Hills, California. A card the size of a business card slides into an ordinary workstation and starts classifying images. It draws less than a third of a watt. No fan. No cloud round-trip. No GPU. The card is BrainChip's AKD1500 PCIe development board, put on sale September 17. It is the third shipping format of the same chip in under three months — after production silicon in June and a gum-stick M.2 module in July. For a decade, neuromorphic computing — silicon that fires only when its input changes, the way neurons do — has been the technology that is always five years from mattering. In 2026 the open question is no longer whether the idea works. It is whether anyone buys it. ## From sample to shelf TIMELINE: BrainChip's AKD1500, lab to shelf ───────────────────────────────────────────────────────────── 2022 ──────── 2025 ──────── 2026 ──────── 2026 ──────── 2026 ✅ 🔬 🧪 🏭 ◉ NOW AKD1000 AKD1500 AkidaTag Production PCIe card released unveiled + RF/ASIC shipments on sale at EW NA partners (Jun 30) (Sep 17) AKD1500 product announcements, 2022–2026. 🎯 The company has crossed from licensing designs to selling parts. The AKD1500 runs useful inference on under 300mW, a fraction of a GPU's idle draw. The test ahead is commercial, not technical: whether designers pick a new architecture over a familiar one. ## The production turn BrainChip announced commercial availability and initial production shipments of the AKD1500 on June 30. The chip is built by GlobalFoundries on its 22-nanometer fully depleted silicon-on-insulator (FD-SOI) process — a mature, low-leakage node chosen for efficiency rather than peak clock speed. The numbers are the pitch. In PCIe mode the part draws under 300 milliwatts; in serial mode, under 200. The company says that delivers near-tera-operations-per-second (TOPS) efficiency in a device that can sit on a battery. The formats followed fast. An M.2 2230 module — 22 by 30 millimeters, the footprint normally reserved for storage or a modem — shipped with a peak rating of 800 giga-operations per second (GOPS) at 4-bit integer precision, 1 megabyte of on-chip memory, and a typical draw of 250 milliwatts. It runs fanless from 0 to 70 degrees Celsius. The PCIe card, announced September 17, puts the same co-processor into any desktop, workstation, or single-board computer with a free slot. It is a developer tool, priced for evaluation rather than deployment, but it closes the loop: a designer can now buy the chip, the module, and the board. > The commercial availability of the AKD1500 production chip marks a profound milestone in our commercialization roadmap.— Sean Hehir, CEO, BrainChip As we wrote in September, the AI compute economy has flipped from training to serving. The serving layer is where power budgets are won or lost — and it is exactly the layer it is now selling into. ## Why sub-watt matters Event-based inference is a different bargain from GPU inference. A conventional accelerator evaluates the whole network on every frame, whether or not anything changed. Akida's fabric computes only on events — a pixel that shifted, a vibration that crossed a threshold — so idle costs almost nothing. #### Where event-based silicon wins — and where it doesn't **Wins:** always-on sensing, wearables, industrial condition monitoring, radar and RF classification, battery-powered vision — anywhere power and thermal headroom, not raw throughput, is the constraint. **Loses:** large-model training, dense floating-point workloads, and anything that already has a wall socket and a cooling fan. A GPU still owns the data center. That trade-off defines the market. For a data center, 250 milliwatts is noise. For a drone, a hearing aid, or a sensor on a factory floor, it is the difference between shipping and not shipping. Power is a systems problem before it is a silicon problem. Cut the draw and you cut the battery, the heat sink, and the size, weight, power and cost (SWaP-C) envelope that governs defense procurement. That is why BrainChip's earliest production customers are in defense and wearables, not cloud infrastructure. ## From radar to orbit In June, BrainChip released a communication reference platform for RF signal classification, aimed squarely at defense contractors and government agencies working under tight SWaP-C limits. A radar that can tell a drone from a bird, on-device and at under 2 watts, is a different product from one that streams raw data to a server. The partnership list reads like a map of where the company expects demand. ForwardEdge ASIC, a subsidiary of Lockheed Martin, is embedding Akida into future custom silicon for aerospace and defense signal processing. Frontgrade Gaisler is exploring the integration of the Akida core into fault-tolerant, radiation-hardened microprocessors for space. Neuromorphyx built an embedded developer board around the part, and IBM folded it into its Spectrum Symphony workload manager. None of these are large revenue events yet. They are options — and they are being written across three of Nexithon's coverage areas at once, which is the more interesting signal. ## Turning points The company's history explains the shape of the bet. It spent years selling Akida as licensable intellectual property, a model that depends on someone else deciding to tape out a chip. The AKD1500 reverses that: it builds the part, stocks the shelf, and takes the inventory risk itself. ⚠️ **The catch** Selling silicon is capital-intensive and slow. A $25 million raise in December 2025 funds the ramp, but the neuromorphic market is still an option on a future, not a present-day cash flow. Revenue follows design wins, and design wins follow developer adoption. The $25 million capital raise, announced ahead of CES in December 2025, was framed around exactly that shift — funding chips and modules rather than pure research. ## The investment question Grand View Research projects the neuromorphic computing market will reach $20.27 billion by 2030, growing at roughly 19.9% a year from 2024. Treat the number with the caution every forward market estimate deserves. It describes a category, not a company. <300mW PCIe-mode power #### AKD1500 draw, PCIe mode Serial mode is under 200mW; the M.2 module runs at 250mW typical. · _BrainChip, 2026_ The competitive field is real. Innatera raised a Series A for neuromorphic microcontrollers aimed at sensors, and NVIDIA's edge modules set the default that most designers reach for. Its advantage is that it ships a commercial part today; its disadvantage is that a new architecture has to be learned before it can be used. The PCIe card is a quiet move against that disadvantage. Put the silicon in a slot, hand developers open tools with no license fee, and let them discover whether their workload fits. That is how a niche architecture becomes a default — or fails to. It has done the hard engineering part. The commercial part is now a question of adoption, and adoption is decided one design win at a time. ## Sources BrainChip launches the AKD1500 PCIe development card The primary announcement behind this article's event anchor: the third shipping format of the AKD1500, on sale September 17, 2026. BrainChip Holdings The company's own newsroom — primary source for launch dates and specifications. BrainChip unveils a PCIe development card for edge AI testing Independent trade coverage of the PCIe card and what it means for developer access to neuromorphic silicon. New Electronics Independent confirmation that the launch was picked up beyond the company's own channels. Commercial availability and production shipments of the AKD1500 The June 30, 2026 production announcement — process node, power figures, and qualification across packaged silicon and bare die. BrainChip Holdings Source for the 22nm FD-SOI process and the sub-300mW power figures.

BrainChip's AKD1500 neuromorphic AI co-processor now ships on M.2, PCIe, and Arduino-sized boards starting at $99, delivering 800 GOPS at under 300 mW with open-source Linux drivers.

Back in November 2025, we wrote about the Brainchip AKD1500 PCIe/SPI Edge AI co-processor designed for low-power systems and delivering up to 800 GOPS at just 300 mW. In recent months, the company has launched several hardware platforms based on the ADK1500. At the end of July, Brainchip introduced the AKD1500 M.2 B+M Key development card for $129, shortly followed by the $99 BrainBoard 1500 module in August, and a few days ago, the company started selling the AKD1500 PCIe development card for $149. Brainchip AKD1500 M.2 B+M Key development card Specifications: AI accelerator – AKD1500 neuromorphic co-processor Akida Neuron Fabric clocked at 5 to 400 MHz 1MB on-chip Local memory 800 GOPS at less than 300 mW Adaptive on-chip learning, no cloud required Package – 7×7 mm MFCTFBGA169 package, 0.5 mm pitch Process – 22 nm FD-SOI CMOS digital logic process Host Interface – B+M Key edge connector for [...] The post Brainchip AKD1500 M.2 and PCIe Edge AI cards, BrainBoard 1500 SPI module now available for $99 and up appeared first on CNX Software - Embedded Systems News.

Back in November 2025, we wrote about the Brainchip AKD1500 PCIe/SPI Edge AI co-processor designed for low-power systems and delivering up to 800 GOPS at

A BrainChip libertou a stack de software do chip neuromórfico AKD1500 e uma biblioteca compatível com Arduino. SNN em hardware exótico passa a falar a língua dos makers.

BrainChip partnered with Neuromorphyx to create the BrainBoard1500, a compact developer board for the AKD1500 neural-network accelerator.

BrainBoard1500 is a compact, standalone AKD1500 co-processor board designed and manufactured by BrainChip's go-to-market partner, Neuromorphyx. Attach it to your MCU or SoC over SPI/QSPI, load an Akida-compatible model into on-board flash, and profile inference in real time with the on-board two-channel power monitor.

BrainChip partnered with Neuromorphyx to create the BrainBoard1500, a compact developer board for the AKD1500 neural-network accelerator.

Neuromorphic Edge AI chips mark a fundamental departure from traditional silicon, utilizing brain-inspired, event-driven architectures to enable real-time inference within milliwatt-level power budget...

BrainChip AKD1500 Neuromorphic Co-Processor Chip is now discoverable in Supplyframe Design Modeler software, a partnership intended to reduce friction for engineers incorporating neuromorphic compute into designs. Supplyframe will provide verified schematic symbols, PCB footprints and 2D/3D ECAD models for the AKD1500, streamlining the cycle from part discovery to schematic integration.

BrainChip’s new AKD1500 co-processor delivers a significant leap in edge AI performance with ultra-low power consumption, enabling advanced AI capabilities in battery-powered devices and thermally-constrained environments. This innovative chip allows for independent AI upgrades to existing systems—from wearables to industrial sensors—without costly full redesigns.

BrainChip AKD1500 is an ultra-efficient Edge AI co-processor delivering up to 800 GOPS while consuming just 300 mW of power, making it suitable for