[Blog] From Edge to Cloud: Rethinking AI System Design with Companion FPGAs

As AI adoption accelerates, workloads are no longer confined to centralized datacenters. Instead, AI is scaling across cloud infrastructure, edge systems, industrial platforms, robotics, and physical AI devices. This shift is fundamentally changing how systems are designed. While CPUs, GPUs, and other accelerators continue to anchor AI performance, modern architectures are becoming more modular, more distributed, and far more dependent on the silicon that surrounds those primary compute engines.

In a recent Six Five interview, Lattice Semiconductor CEO Ford Tamer described this evolution as a move away from viewing AI systems purely through the lens of peak compute. As AI scales across heterogeneous environments, architectural value increasingly comes from how systems are controlled, secured, connected, and orchestrated over time. This is driving higher attach rates for companion silicon that works alongside CPUs and GPUs, handling the system level functions that enable reliable and scalable deployment from edge to cloud.

At the same time, primary processors are being burdened with an expanding set of non-core responsibilities such as security enforcement, power management, sensor aggregation, and interface bridging. Combined with faster platform cycles, rising security threats, and long lifecycle requirements in regulated markets, these demands are pushing traditional architectures to their limits.

This is where field programmable gate arrays, or FPGAs, are taking on a new role. Rather than competing with processors as accelerators, low power FPGAs are increasingly deployed as purpose-built companion chips. By offloading deterministic control, security, and I/O functions, FPGAs complement primary processors to help simplify system design, improve efficiency, and future proof AI deployments as architectures continue to evolve.

Understanding FPGAs as Companion Chips
FPGAs can be leveraged as companion chips in AI systems by surrounding and supporting primary compute devices rather than competing with or replacing them.

FPGAs act as critical support players, helping offload tasks from processors and enabling them to operate more efficiently. And, while they aren’t the only companion chip option available, their unique feature set makes them an ideal choice. Such features include:

• Programmability and adaptability. FPGAs can be used for a wide range of purposes and reprogrammed after deployment to meet evolving system needs. They can be used for a wide range of non-core, but still critical tasks, such as power sequencing and management; system control; security enforcement and secure boot; interface bridging; and legacy I/O support.
• Crypto-agility and built-in security. Select FPGAs support post-quantum readiness and enable algorithm updates without requiring a full hardware redesign.
• Low Latency, deterministic, parallel processing. FPGAs can process multiple compute tasks in parallel, which is especially valuable in demanding/limited ecosystems like edge AI, industrial devices, robotics, and sensor-driven solutions.
• Processor-agnostic operations. FPGAs are heterogenous and can work across today’s most popular GPUs, CPUs, NPUs, and SoCs.

“If you open up any server box—whether it be AI or general purpose—you're going to see Lattice [devices] all over the place doing things like power management, power sequencing, interfacing, and security,” said Esam Elashmawi, Lattice Chief Strategy and Marketing Officer, in his recent LinkedIn Live panel. Paired effectively with central processing chips, these FPGAs can become a unified companion layer that connects compute, security, I/O, and sensors wherever the AI workload runs.

How companion FPGAs enable AI systems from edge to cloud
Across modern AI deployments, companion FPGAs serve as a unifying system layer that supports primary compute devices without replacing them. Their role varies by environment, but the architectural pattern remains consistent: offload complexity, preserve determinism, and provide continuity as processors and workloads change.

• In datacenter and cloud AI infrastructure, companion FPGAs are commonly used for board-level control and management functions, such as power sequencing, system bring up, health monitoring, and secure boot. These tasks are critical to system reliability but sit outside the core compute path. By assigning them to an FPGA that is first-on and last-off, designers can maintain a stable control and security foundation across processor refresh cycles, even as CPUs and GPUs evolve. This approach reflects a broader industry shift toward treating companion silicon as foundational to AI infrastructure, rather than optional.
• At the edge, where AI systems must interact directly with the physical world, companion FPGAs often take on additional real time responsibilities. A concrete example is the Advantech MIC-FG-HSB solution, an FPGA-powered sensor over Ethernet board that integrates a Lattice CertusPro™-NX FPGA with the NVIDIA Holoscan Sensor Bridge. In this design, the FPGA adapts diverse sensor interfaces such as MIPI and GMSL to high-speed Ethernet, handling deterministic I/O, timing, and data conditioning before data reaches the GPU. By offloading these functions, the primary processor can focus on AI inference and perception, while preserving the low latency and predictability required for physical AI deployments.
• Similar architectural patterns appear in industrial automation, robotics, and automotive platforms. In these environments, companion FPGAs sit between sensors, actuators, and compute engines to ensure deterministic control and real time responsiveness. Latency predictability and system stability are often more critical than raw throughput, particularly in long-lived systems that must operate reliably across multiple processor generations. By anchoring control, safety, and interface logic in an FPGA, developers can integrate new AI processors over time without redesigning the entire system.

Across these use cases, companion FPGAs provide a consistent system layer that spans edge and cloud environments. They enable heterogeneous architectures to scale, adapt, and remain secure as AI workloads expand beyond traditional boundaries.

Capable Companions for Sustainable Growth
As AI architectures continue to scale out, the role of companion chips is becoming increasingly central to system design. While primary processors deliver the compute performance that powers AI models, companion FPGAs handle the control, connectivity, and security functions that keep systems operational, interoperable, and resilient over time.

Low power FPGAs are uniquely suited to this role with their programmability, deterministic behavior, and processor-agnostic operation, allowing them to adapt as architectures evolve, supporting long-lifecycle systems. Rather than competing for attention with CPUs and GPUs, companion FPGAs enable those processors to operate more efficiently by removing non-core burdens from the compute path.

As highlighted in recent discussions across the industry, including Lattice’s LinkedIn Live panel and the Six Five interview, the future of AI infrastructure is not defined by a single dominant chip. It is defined by how well systems are architected as a whole. Companion FPGAs provide the connective layer that allows AI systems to scale sustainably, securely, and intelligently from edge to cloud.

To learn more about FPGAs as companion chips and to begin incorporating Lattice FPGAs as companions in your compute infrastructure, contact our team today.