[Blog] Contextual AI: Enhancing Edge Intelligence with FPGA Technology

Edge AI — implementing AI models on Edge devices to process algorithms locally rather than in a centralized computing location, like the cloud — has garnered significant attention as one of the fastest developing areas of artificial intelligence. Calculated at roughly $21 billion USD in 2024, the Edge AI market is expected to reach over $143 billion by 2034. This signals a sustained focus across industries on the development of AI-powered Edge systems.

New opportunities for Edge AI are exciting and diverse, ranging from self-driving vehicles and smart home devices to automated machinery in industrial settings. However, these systems can present a unique array of challenges for developers in regard to hardware constraints and optimizing power and processing complexity. For example, designers need to ensure that embedded AI models are compact, yet powerful enough to analyze real-time contextual information directly on Edge devices to maximize their performance for latency, bandwidth efficiency, accuracy, and sustainability, all while protecting data privacy and reducing exposure to threats.

The evolution of Edge applications is occurring in tandem with the growth of contextual intelligence, which seeks to understand data in the context of its environment, relationships, and interactions. These combined pursuits have spawned Contextual Edge AI, which runs AI models on Edge devices to help systems process and learn from environmental data and improve performance over time. Being able to effectively process contextual data — such as smart devices using multiple modals of sensing, including vision and audio, to understand the environment around them — is key to Edge devices’ functions and goals to streamline the user experience. And with larger amounts of data being processed on the Edge, these devices require higher levels of power to support their operations.

The flexibility, in-field upgradability, and interoperability of Field Programmable Gate Arrays (FPGAs), combined with low power, low latency, and parallel processing capabilities make them an essential tool for developers looking to overcome these challenges and optimize their Contextual Edge AI applications.

The Challenges of Implementing Contextual Edge AI

By analyzing contextual data directly on Edge devices, systems can make smarter real-time decisions and drive a more symbiotic user-device relationship. For example, smart computer monitors can leverage user presence data, gathered securely through visual sensors, to turn on when users turn towards them, and off when they turn away to optimize power usage. Smart cellular devices can similarly use facial or fingerprint recognition to securely examine biometric or visual user data, access user credentials, and sign in to secure applications.

But while users have come to expect these kinds of seamless, personalized experiences made possible with Contextual Edge AI, developers are likely to face a variety of challenges behind the scenes. These include:

1. Complexity
   As organizations try to further streamline the human-machine interface, the contextual data that is gathered by sensors at the Edge is becoming increasingly complex. This necessitates both AI models and hardware that can handle higher workloads while still maintaining efficiency. Edge AI also requires flexibility, as models and hardware may need to be updated regularly to keep pace with evolving contextual data. Edge AI may also involve the use of TinyAI models, whose compressed algorithms are better suited for high performance in Edge cases including wearable devices, environmental monitoring remote sensors, quality control in industrial IoT applications, and more. Even so, TinyAI models require adequate power and system support to operate effectively.
2. Interoperability
   To collect as much relevant contextual intelligence as possible, Edge networks often include a wide array of sensors, processors, gateways, and servers. These components all need to communicate effectively with one another in order to support real-time results. Edge devices must be able to handle growing AI workloads while still operating in tandem with the other devices in the network, be they existing components or third-party hardware and software. Without flexible hardware, the connection between sensors, Edge devices, and the recipients of data analysis will be unreliable.
3. Energy efficiency
   Advanced AI models require a significant amount of energy to function, with researchers projecting that AI-related electricity consumption is expected to grow by as much as 50% annually from 2023 to 2030. It’s crucial that this power is delivered to models in a consistent, energy-efficient manner. If not configured with efficiency in mind, Edge deployments are likely to experience excess energy usage, drive higher costs, and result in higher latency between the execution of Edge AI actions and the availability of their results.

Only by accounting for these challenges — and working proactively to overcome them — can developers leverage Contextual Edge AI to improve the user experience.

Customizing Contextual Edge AI Implementation with Lattice FPGAs

Overcoming complexity, interoperability, and energy efficiency challenges is a multifaceted effort, requiring flexibility in the application of both hardware and software. AI-optimized, low power Lattice FPGAs and the AI application-specific solution stack, Lattice sensAI™, are well-suited to address implementation challenges and enable Contextual Edge AI applications.

Lattice FPGAs can be configured to perform specific AI tasks, allowing developers to tailor applications to different contexts and address specific Edge data. This enables Edge AI applications to be optimized for maximum efficiency and reliability, all while maintaining the flexibility to adapt the FPGA to support evolving AI models. FPGAs also come with customizable I/O interfaces, which support connectivity to a diverse array of Edge AI applications across devices and environments (e.g., cameras, radar, environmental sensors) and enable more streamlined interoperability.

This customization is also further supported and strengthened by the Lattice sensAI solution stack. Lattice sensAI can take models that have been trained in industry standard AI frameworks such as TensorFlow, Caffe, and Keras, and adapt them to run on FPGA resources with cutting edge technologies like model quantization, pruning, and sparsity exploits. Lattice’s Neural Network Compiler can then analyze the model and make suggestions to run it most efficiently based on the types of circuits and on-chip networks. Also, Lattice Propel and Lattice Radiant design software can be used to create the right combination of circuits to accelerate the running of those models in as power-efficient a manner as possible.

Lattice FPGAs also significantly reduce the latency between sensor data acquisition and processing, enabling faster responses and improved performance for users. Preprocessing and data aggregation tasks can be completed on an FPGA before routing through an AI model or central computing engine, helping to reduce the amount of stress on Edge devices and, in turn, reducing excess energy usage.

By leveraging Lattice FPGAs, various industries can overcome the challenges of resource constraints, energy efficiency, connectivity, and scalability. These programmable devices enable real-time data processing and prediction that is essential for applications in Industrial equipment, medical devices, automotive, and robotics. The adaptability of FPGAs allows for tailored AI solutions that meet the specific needs of various environments, ensuring optimal performance and reliability.

To learn more about how Lattice can enable contextual AI at the Edge, contact our team today, and watch the Dell: Future of AI Based Context Sensing in Edge Devices session from our recent Lattice Developers Conference.