[Blog] From Proof of Concept to Production: Bringing Edge AI to Life

Planning and execution are two very different beasts. A project may appear straightforward on paper, staying within budget, on schedule, and technically sound, only to hit roadblocks in the real world. However, turning ideas into reality isn’t always smooth, and success depends on how well we anticipate and navigate the unknowns.

This gap between concept and execution is especially pronounced in the fast-growing realm of edge artificial intelligence (AI). In a recent roundtable discussion hosted by Embedded Computing Design, experts from Lattice Semiconductor, tinyVision.ai, and Aizip gathered to explore the real-world challenges of bringing edge AI models to production. Their discussion covered the rise of edge AI applications, the difficulty of overcoming scale and compute limitations, and the key role of purpose-built solutions that can deliver in the field.

The Rise of Edge AI
As technology becomes increasingly personalized and interactive, companies are identifying more use cases for on-device intelligence. Whether it’s optimizing industrial processes and systems, enhancing automotive safety, or enabling smarter and more responsive consumer products, edge deployments are moving from more isolated concepts to critical product features.

These pursuits involve the development and deployment of a growing number of edge AI solutions to support near-edge computing and processing tasks, especially in human-machine interfaces (HMIs). In some ways, this is a result of the “AI everywhere” phenomenon occurring across industries and disciplines. AI does, however, bring a number of critical advantages to edge computing. By processing data locally, at or near the sensor, developers can reduce dependency on cloud infrastructure, lower energy consumption, and improve bandwidth, all while strengthening offline functionality, reliability, and real-time responsiveness.

Even so, designing AI models that fit within edge constraints is a difficult task. Traditional models are often large and power-hungry, by no means a match for the low power and computing capacity of edge devices. Moving from today’s model of specialized AI accelerators at the edge to an ultra-efficient “next-to-sensor" compute framework that doesn’t rely on the cloud is easier said than done. It requires developers to design smaller, edge-native AI models that can directly meet the needs of specific edge applications without straining the available power or space. Moving from these designs to actual deployments is where the concept-to-execution gap is most pronounced.

The Challenge of Moving from Proof-of-Concept to Production
While developers understand the need to design efficient and reliable edge AI models, it’s often difficult to bridge the gap between proof-of-concept (PoC) and model execution. It’s the newest version of an age-old problem: anyone can design their own PoC, but can they take that model and scale it to a full production-level solution?

Bringing edge AI PoC prototypes into real-world environments is a process often beset by difficult challenges, including:

• Moving data in and out of Systems on Chip (SoCs).When working with various sensors, protocols, interfaces, and high pin counts, moving data between edge components and AI models can become very complicated. Figuring out how to connect these disparate data sources to the edge processing model requires careful planning and execution and needs to be replicable at scale.

• Radical differences between PoC and production environments. PoCs are built to examine, test, and demonstrate edge algorithms. Because of this, they have a wealth of resources—compute power, energy, money, space—that the actual edge devices will not be guaranteed to have. When these PoCs rely on cloud-based processing, oversized hardware, or unoptimized models, they’re not going to successfully translate to production.

• Radical differences between PoC and production environments. PoCs are built to examine, test, and demonstrate edge algorithms. Because of this, they have a wealth of resources—compute power, energy, money, space—that the actual edge devices will not be guaranteed to have. When these PoCs rely on cloud-based processing, oversized hardware, or unoptimized models, they’re not going to successfully translate to production.

• Lab data rarely reflects real-world complexity. Beyond having additional technical resources, edge AI PoC models are often trained on synthetic datasets in curated testing environments. While this is helpful for working out development challenges, it’s often not reflective of the edge cases, sensor noise, and overall variability found in the field. Take a home security system, for example—while it may be trained to recognize synthetic “glass shattering” sounds, the real-world audio is guaranteed to have more variety than the test dataset.

Creating Purpose-Built AI Solutions

These challenges make one thing clear: edge AI architectures must be purpose-built with deployment in mind from the outset, not developed in a vacuum and deployed with crossed fingers. Unlike general-purpose AI models, edge deployments require solutions that are optimized for their specific tasks, operating conditions, and resource limitations. By starting from the ground-up rather than shrinking existing models down, teams can save time, money, and development resources, as well as support more targeted and use-case-specific operations.

To support edge developers, Lattice has worked with its partners to create pre-validated solutions like the Lattice sensAI™ solution stack that includes proven hardware, pre-trained models, and helpful development tools. These solutions offer reliable building blocks for developers to build upon and customize for specific use cases, helping to accelerate development and reduce risk.

Both tinyVision and Aizip have leveraged Lattice solution stacks and Field Programmable Gate Arrays (FPGAs) to create purpose-built, AI-enabled edge HMI systems:

• tinyVision's smart glasses solution leverages a Lattice CrossLink™-NX FPGA to bridge and synchronize a camera sensor and heads-up display within an extremely limited amount of space. By designing around the FPGA as a flexible, low cost, and small form factor aggregator, these glasses can execute critical computing and sensor-fusion needs at the edge without requiring a massive amount of power and/or physical space.
• Aizip’s vocal recognition system also leverages a CrossLink-NX FPGA to help support personalized vocal recognition and processing. By designing an AI model within the constraints of the FPGA, developers can bridge microphone sensors and computing capabilities and enable tasks like tailored vehicle in-cabin person recognition.

Given their inherent small form factor, low power consumption, and pre- and post-deployment flexibility, FPGAs like CrossLink-NX are a powerful component of purpose-built edge AI developments. Developers can easily configure these chips to match the computing needs of a specific edge application, helping fuse data from disparate sensors, accelerate processing workloads, and handle custom I/O protocols with minimal latency. The FPGA’s parallel processing capacity also enables local processing while transmitting only the necessary data to displays or other components, reducing bandwidth usage and improving overall privacy.

Supporting Innovation at the Edge
Bringing edge AI models from concept to production requires more than a functioning prototype. Success hinges on designing solutions that leverage proven hardware and software to account for real-world expectations and constraints from the start, rather than adapting lab-tested models after the fact. With purpose-built solutions rooted in dynamic FPGAs, developers gain the flexibility and efficiency they need to design reliable and scalable edge AI applications.

To learn more about designing for the edge, you can watch the full roundtable discussion here. If you’d like to explore proven FPGA-based building blocks for your next edge application, download our HMI solution brief or contact us today.