[Blog] Designing Low Power, Real-Time AI at the Far Edge

Supporting today’s growing landscape of distributed, autonomous devices is no simple feat. Whether it is industrial robots, autonomous drones, or in-vehicle safety systems, each of these increasingly intelligent solutions requires real-time processing capabilities to function.

Supporting these capabilities requires moving artificial intelligence (AI) and machine learning (ML) applications away from centralized cloud services and closer to the cameras, radar systems, and other sensors that gather critical data at the edge.

This, in turn, increases the processing, computing, and power demand imposed on edge devices that are already burdened with tight power and thermal budgets. Supporting real-time, deterministic behavior at the edge requires the support of low power, efficient, and reliable AI applications—and that’s where the Lattice sensAI^™ solution stack can empower edge infrastructure success.

Designed to support low power, small Field Programmable Gate Array (FPGA)-based AI and ML applications at the far edge, Lattice sensAI provides developers with a combination of FPGA hardware, software tools, and IP cores to help deploy AI inferencing capabilities in embedded systems.

With the latest version of Lattice sensAI, Lattice introduced enhanced capabilities that enable smart, scalable AI at the edge. A further examination of realworld applications now illustrates the impact of streamlined design and deployment on modern edge use cases.

To dive deeper into sensAI’s edge enablement capabilities, we recently published the following detailed white papers:

Low Power, Real-Time AI for the Far Edge
As AI inference capabilities continue to shift closer to sensors, many edge systems are starting to encounter the limits of more conventional architectures. GPU and SoC based approaches often struggle to meet the high power, thermal, and deterministic requirements of always-on edge workloads.

This white paper goes beyond recent enhancements to explore the details and relevant applications of the sensAI solution stack for far-edge deployments. It demonstrates sensAI’s capacity to act as an end-to-end solution that combines software tooling, purpose-built models, and low power FPGA hardware to support deterministic edge AI inferencing within sub-watt power environments. By leveraging these solutions, developers can design and deploy efficient edge AI applications across automotive, industrial, robotics, and human-machine interface (HMI) applications.

Other key themes include:

• Expanded support for modern embedded vision and HMI architectures.
• A unified, production-ready Model Zoo that includes purpose-built model families made for deployment, rather than just experimentation.
• Accurate tooling that aligns training, quantization, simulation, and hardware execution for effective deployments.
• Enhanced scalability and longevity through the capacity for field updates over longer product lifecycles.
• An exploration of Lattice’s Golden AI Reference Design (GARD) architecture for integrating sensAI into hardware and firmware.

Download the full white paper here: Low Power, Real-Time AI for the Far Edge

Multi-Object Detection at the Far Edge with Lattice sensAI 8.0
Multi-object detection (MOD) is one of the most computationally demanding, difficult to scale vision workloads applied on far edge devices. Unlike simple classification or single-object workloads, MOD must be able to simultaneously identify, localize, and classify a range of varied objects in real-time, all while staying within power constraints.

This white paper examines why MOD challenges persist in embedded deployments and explores how the sensAI solution stack supports effective, deterministic deployments on low power FPGA platforms. It highlights two specific models included in the sensAI 8.0 Model Zoo, namely a Generic MOD model and an Automotive-focused MOD model and explores their specific applications for MOD enablement. Both models demonstrate how a common detection pipeline can be adapted to different application requirements without sacrificing determinism or edge efficiency.

Other key themes include:

• Market and technology drivers for MOD deployments.
• Architectural commonalities and differences between the Generic MOD model and Automotive-focused MOD model.
• How to interpret key performance metrics for edge MOD deployments.
• Best practices for choosing the right MOD model for your specific project.

You can download the full white paper here: Multi-Object Detection at the Far Edge with Lattice sensAI 8.0

Exploring Further Applications of the sensAI Solution Stack
Edge AI application success ultimately depends on efficiency, determinism, and scalability. The Lattice sensAI solution stack enables these requirements, supporting both broad application needs and specialized workloads through a unified feature set and enhanced software-hardware co-design framework.

Click here to learn more about the Lattice sensAI solution stack. To further explore how Lattice’s portfolio of edge AI FPGA solutions can help you build smarter edge applications, contact us today.