Chris Anastasi, Applications Engineer 

Raspberry Pi developers, makers and hobbyists interested in creating Edge AI use cases need to look no further than the BrainChip neuromorphic add-on cards.  As interest grows for efficient, scalable AI solutions at the edge, developers are seeking hardware platforms that combine accessibility with advanced performance at a Maker price point, BrainChip is meeting that need by integrating its Akida neuromorphic technology into familiar, compact systems—making it easier than ever to prototype and deploy intelligent applications. 

BrainChip provides the AKD1000 PCIe card and the AKD1000 M.2 cards, development boards that incorporate a neuromorphic processor.  These can be bundled into development kits, including a small form factor PC and the Raspberry Pi 4 both with the AKD1000 PCIe card and the Raspberry Pi 5 with the AKD1000 M.2 card.  Paired with the Akida MetaTF tools available on our Developer Hub (developer.brainchip.com), these platforms empower customers to rapidly develop product prototypes, showcasing Akida’s edge AI capabilities for their specific use cases.   These development kits offer a quick evaluation of BrainChip Akida’s neuromorphic technology, enabling conversion of CNN networks to execute on an event based neural network. 

Raspberry Pi 5 has taken the world of single-board computers by storm, offering substantial upgrades in processing power, RAM, and connectivity. This new model paves the way for more advanced applications in fields like IoT, AI, and edge computing. However, while the Pi 5 is powerful, there’s always room for more — and that’s where the AKD1000 Neuromorphic Processor M.2 Card comes in. This integration opens exciting possibilities for developers, particularly in the field of machine learning (ML) and artificial intelligence (AI). 

The Raspberry Pi 5 comprises a 64-bit quad-core ARM Cortex-A76 processor, dual 4K HDMI outputs, and Gigabit Ethernet. These upgrades make it an ideal candidate for running more demanding tasks like AI processing, but its processing capabilities are still limited by traditional CPUs and GPUs. Introducing the AKD1000 Neuromorphic Processor M.2 Card. This accelerator card is designed to handle complex AI workloads efficiently by mimicking the brain’s structure, using a neuromorphic architecture that provides 1.5 TOPS of high parallelism and low power consumption of a few watts. By integrating the AKD1000 with the Raspberry Pi 5, developers can offload AI tasks to the processor, boosting performance while keeping the overall system energy efficient. 

The AKD1000’s integration with the Raspberry Pi 5 creates a powerful combination for edge computing. Edge computing is essential for real-time, low-latency applications, such as video analytics, facial recognition, and sensor data processing. The ability to run AI models locally on the device, without needing to communicate with cloud servers, significantly reduces response time and bandwidth consumption. 

Take IoT applications, for example: A Raspberry Pi 5 paired with the AKD1000 can be used to run machine learning models directly on-site, enabling smart devices to make real-time decisions without relying on external servers. This could revolutionize industries like healthcare (e.g., telemedicine and diagnostics), manufacturing (e.g., predictive maintenance), and even education (e.g., AI-powered robotics and learning tools). 

Integrating the AKD1000 with the Raspberry Pi 5 is straightforward with the Raspberry Pi HAT (Hardware Attached on Top) is a standardized add-on board designed to be attached to a Raspberry Pi single-board providing the M.2 slot on the Raspberry Pi 5 used to connect the AKD1000, and with the right drivers and software, the two components communicate seamlessly. This allows the Raspberry Pi to offload AI-intensive tasks to the AKD1000 processor, allowing the system to handle more complex workloads without bogging down the Pi’s primary processor. 

The combination of the Raspberry Pi 5 and AKD1000 is just the beginning. As AI and edge computing continue to grow, the ability to integrate specialized processors like the AKD1000 will become even more critical. This modular approach allows developers to customize their systems for specific applications, creating highly flexible, powerful platforms. 

This integration sets the stage for a new wave of AI-powered edge computing applications, from smart cities and healthcare to advanced robotics. Developers now have the opportunity to push the boundaries of what’s possible with the Raspberry Pi 5, taking full advantage of its affordability, versatility, and newfound processing power. 

As an AE/FAE providing design-in assistance and technical support for edge AI applications I’ve found that these development kits make my job—and our customers’ jobs—faster, easier, and most importantly fun! 

For developers seeking scalable, efficient AI at the edge, this combination offers a compelling foundation to build, test, and deploy. Whether you’re working on robotics, IoT, or any other AI-driven project, this pairing offers a cost-effective, powerful solution that can handle the most demanding tasks. The future of edge AI is now within reach, and with the right tools and integration, developers can create the next generation of intelligent, real-time systems.   

Come to BrainChip’s Developer Hub at developer.brainchip.com and use the discount code RaspberryPi25 to get a real deal on a neuromorphic processor for your very own Raspberry Pi.