The gap between planning and execution is often vast. A project that seems straightforward in theory, even with a controllable budget, orderly progress, and feasible technology, can encounter numerous obstacles once implemented. Transforming an idea into reality is not always smooth sailing; success largely depends on our ability to anticipate and navigate the unknown.
In the rapidly evolving field of edge artificial intelligence (AI), the disparity between concept and implementation is particularly pronounced.
The Rise of Edge AI
As technology continues to evolve towards personalization and interactivity, companies are discovering more intelligent application scenarios at the device level. Whether used to optimize industrial processes and systems, enhance automotive safety features, or create smarter, more responsive consumer products, edge deployment is transitioning from an isolated concept to a critical product feature.
This requires the development and deployment of an increasing number of edge AI solutions to support near-edge computing and processing tasks, especially in the field of human-machine interfaces (HMIs). To some extent, this is a result of AI permeating various industries and disciplines. Indeed, AI brings numerous key advantages to edge computing. By performing localized data processing within or near sensors, developers can reduce reliance on cloud infrastructure, lower energy consumption, and improve bandwidth efficiency, while enhancing offline functionality, reliability, and real-time responsiveness.
Nevertheless, designing AI models that meet the constraints of edge devices is still a challenging task. Traditional models are often large and power-hungry, making them unsuitable for the low power and limited computational requirements of edge devices. Transitioning from models that deploy dedicated AI accelerators at the edge to ultra-efficient “near-sensor” computing architectures that do not rely on the cloud is easier said than done. This requires developers to design smaller, edge-native AI models that precisely meet the needs of specific edge applications without exhausting the already limited power and space. The process from these designs to actual deployment is where the gap between concept and implementation is most pronounced.
Challenges from Proof of Concept to Production
While developers understand the need to design efficient and reliable edge AI models, the gap between proof of concept (PoC) and model deployment is often difficult to bridge. This issue has existed for a long time, simply rephrased: building a PoC model is not difficult; anyone can design it. The key is whether the model can be implemented and scaled into a complete production-grade solution.
The process of introducing the PoC prototype of edge AI into real application scenarios typically faces numerous severe challenges, including:
• Importing/exporting data to system-on-chip (SoC). When dealing with various sensors, communication protocols, interfaces, and a large number of pins, transferring data between edge components and AI models becomes quite complex. Connecting these different data sources to the edge computing model requires meticulous planning and execution, and must be scalable.
• Significant differences between PoC and production environments. PoC aims to validate, test, and demonstrate edge algorithms, thus it is configured with ample resources, including computing power, energy, funding, and space, while actual edge devices may not have these resources. If these PoCs rely on cloud processing capabilities, oversized hardware, or unoptimized models, they will struggle to successfully transition into concrete production-grade solutions.
• Laboratory data often fails to reflect the complexity of real-world scenarios. In addition to having extra technical resources, edge AI’s PoC models typically use synthetic datasets and are trained in simulated testing environments. While this helps address challenges encountered during the development phase, it often does not reflect the edge use cases, sensor noise, and overall variability found in the field. For example, in a home security system, while the model may be trained to recognize synthetic “glass breaking” sounds, the sounds in a real environment will inevitably be more diverse than those in the test dataset.
Building Dedicated AI Solutions
The challenges outlined above clearly indicate that edge AI architectures must be tailored for final deployment from the outset, rather than designed in isolation from the actual environment and then deployed with fingers crossed. Unlike general AI models, edge deployment scenarios require solutions to be deeply optimized for their specific tasks, operating conditions, and resource constraints. By building from the ground up rather than simply compressing existing models, development teams can effectively save time, costs, and development resources, thereby supporting more targeted and practical applications.
To support edge developers, Lattice Semiconductor has partnered to launch several pre-validated solutions, such as the Lattice sensAI™ solution set, which includes validated hardware, pre-trained models, and practical development tools. These solutions provide developers with reliable building blocks for constructing and customizing specific application scenarios, helping to accelerate development and reduce risk.
Empowered Dedicated Edge HMI Systems:
• The tinyVision smart glasses solution utilizes Lattice CrossLink™-NX FPGA to bridge and synchronize camera sensors and heads-up displays within extremely limited space. By designing the FPGA as a flexible, low-cost, small-sized aggregation hub, these smart glasses can perform critical computations at the edge and support sensor fusion without consuming excessive power and/or physical space.
• Aizip’s voice recognition system also employs CrossLink-NX FPGA to support personalized voice recognition and processing. By designing AI models that fit the constraints of FPGA devices, developers can connect microphone sensors and computational capabilities to achieve tasks such as customized in-vehicle personnel recognition.
With their small size, low power consumption, and flexibility before and after deployment, FPGA products like CrossLink-NX have become essential components for dedicated edge AI development. Developers can easily configure these chips to meet the computational needs of specific edge applications, integrate data from different sensors, accelerate processing speed, and handle custom I/O protocols with extremely low latency. The parallel processing capabilities of FPGAs help achieve localized processing while only transmitting necessary data to displays or other components, thereby reducing bandwidth usage and enhancing overall privacy and security.
Supporting Edge Innovation
To transform edge AI models from concept to product, merely having a functional prototype is far from sufficient. The key to success lies in designing based on mature hardware and software from the initial stages, ensuring that the solution truly meets the demands and constraints of real-world scenarios, rather than retrofitting models that have only been validated in the lab. With dedicated edge AI solutions based on dynamic FPGAs, developers can gain the flexibility and efficiency needed to design edge AI applications that are reliable and scalable.
