Brainchip MetaTF Software Tools ease the Transition of ML Models to the Edge

Enabling Effortless AI Transition to the Edge

The global market for edge AI is estimated to reach $143.6 billion by 2032. This growing adoption emphasizes the need for businesses to adopt edge-based machine learning solutions. Companies that adopt edge AI will gain a competitive edge, especially those that invest in advanced neural network hardware. 

BrainChip has introduced its MetaTF Software Tools to simplify the migration of ML models to the Akida event-based computing platform. MetaTF offers a set of tools for converting existing models so they can work with BrainChip's Akida processors.

Key features of MetaTF include:

• Effortless Model Transition: It offers tools to convert, optimize, and deploy ML models for Akida processors.

• Reduced Computational Load: Akida computing focuses only on relevant event data. This helps to reduce computational overhead as compared to traditional NPUs.

• Familiar Development Environment: It allows developers to work in familiar environments like Jupyter Notebooks and experiment with ML on the edge. This can help to minimize the learning curve.

• Enhanced Model Validation: It offers a simple pathway for companies to test and refine their models on advanced processors.

With these capabilities, MetaTF helps companies to adopt edge AI quickly and efficiently. It also helps them find new opportunities for innovation and performance.

The Unique Challenges of Embedded Devices

Deploying ML solutions on embedded devices comes with challenges that are different from traditional server. Embedded and IoT devices, ranging from sensors and smart phones, are highly diverse. They do not work like servers that follow standard architectures. These devices have their own architecture and operating systems. Also, they may sometimes have no OS.

This diversity makes installing and packaging software more complex, especially when they have to scale it to multi-million devices. Software-build technologies are important to address these challenges. They:

• Simplify the management of complex hardware configurations.

• Ensure repeatability in software deployments.

• Enable the creation of consistent images throughout different devices.

These tools are important for overcoming the unique challenges of deploying ML solutions in embedded environments.

Simplifying Edge Deployment with Edge Impulse and MetaTF

Training ML models for compact, low-power devices is only part of the challenge. Packaging and deploying these models on edge devices present additional hurdles. BrainChip has significant experience with popular embedded systems like Buildroot and Yocto. BrainChip Akida System on Chip (SOC) integrates smoothly into different embedding environments of customers.

By joining hands with Edge Impulse, BrainChip simplifies ML workflows to edge. It also simplify the deployment of event-based AI solutions. Together, they make it easier than ever to implement event-based ML solutions on embedded devices.

One standout example of this collaboration is the Akida™ Edge Box. This was created by cooperation with Edge Impulse and VVDN. This solution showcases BrainChip’s expertise in addressing the unique challenges of edge AI deployment by integrating advanced technologies. The key features of Edge Box include:

• Hardware Configuration: Built on an NXP i.MX 8M application processor. The Edge Box includes dual Akida 1000 chips integrated via a single M.2 PCI card.

• Streamlined Deployment: MetaTF libraries and NXP’s i.MX Yocto Project Board Support Package help integrate models trained in Edge Impulse Studio.

• Real-World Applications: It shows how event computing and embedded system tools can work together for better AI solutions.

Contact BrainChip's architecture team for more information on integrating BrainChip’s event-based AI into embedded systems or to discover the potential of the Akida Edge Box. 

Bring your AI initiatives to the edge with BrainChip!