AI’s Growing Role in FPGA Design and the Future of Programmable Logic

Artificial intelligence (AI) is increasingly impacting the design and management of Field Programmable Gate Arrays (FPGAs), offering the potential to simplify and accelerate portions of the design process. While FPGAs remain crucial in sectors demanding adaptability—like life sciences, AI processing, automotive, and 5G/6G communications—their complexity has historically been a barrier to wider adoption. Recent advancements in AI and machine learning (ML) are beginning to address these challenges, promising to unlock further potential for programmable logic.

The Challenge of FPGA Programming

Traditionally, FPGAs have been programmed using hardware description languages like Verilog and VHDL, requiring specialized expertise. This complexity contrasts with software development, where higher-level languages and more abstract tools are commonplace. Andy Nightingale, vice president of product management and marketing at Arteris, highlights this difference: “You require RTL skills to program an FPGA versus programming software to run on the GPU to do a similar task.”¹ This skill gap has limited the broader application of FPGAs beyond prototyping and niche applications.

AI-Powered Design Tools Emerge

AI is now being integrated into FPGA design flows in several ways. Agentic AI, in particular, is showing promise in automating aspects of the design process. Venkat Yadavalli, head of the Business Management Group at Altera, envisions a future where AI agents can translate various inputs—including code, voice, diagrams, or schematics—into functional code.¹ This could significantly lower the barrier to entry for designers unfamiliar with traditional FPGA development methods.

AI-native compilers are emerging that can infer intent from high-level code or natural language, automatically generating Register-Transfer Level (RTL) or High-Level Synthesis (HLS) code and optimizing mapping, pipelining, and timing closure.¹ ChipAgents is one company developing such compilers, aiming to adapt pipelines in real-time as model architectures and operators change.¹

Bridging the Gap Between AI Frameworks and FPGA Hardware

Deploying AI workloads onto FPGAs has also become easier with tools like Xilinx’s Vitis AI, which helps bridge the gap between frameworks like PyTorch and the AI Engine within the FPGA fabric.¹ AMD’s Rob Bauer notes that compiler optimizations and quantization techniques are crucial for efficiently mapping AI models onto FPGA silicon.¹

The Role of High-Level Synthesis and Mixed-Signal Co-Design

High-level synthesis (HLS) allows designers to leverage languages like C or C++ to describe hardware functionality, simplifying the programming process. AI is also contributing to mixed-signal co-design, where ML algorithms can automatically tune Digital Signal Processing (DSP) algorithms based on analog simulation data.¹ Amol Borkar, senior director of product management and marketing at Cadence, explains that this reduces design cycles and helps engineers balance analog precision with DSP complexity.¹

Challenges and Future Directions

Despite these advancements, challenges remain. Optimizing FPGAs for specific workloads requires balancing power, performance, and latency, particularly in cost-sensitive embedded applications.¹ The rapid evolution of AI models also necessitates adaptable hardware solutions. As Baya Systems’ Nandan Nayampally points out, programmability is key to accommodating changing models and architectures.¹

The industry is moving towards a software-first design methodology, requiring advanced memory compilers that can quickly adapt to unique AI algorithm requirements.¹ Synopsys’ Daryl Seitzer highlights the need for flexible configurations, ultra-low voltage support, and multi-port options in these compilers.¹

the vision is to enable a seamless programming experience where developers can compile code to run on CPUs, GPUs, or FPGA fabric with equal ease. As Yadavalli states, “That’s the incredibly long-term vision. Everybody who’s playing in this space is making progress in that direction.”¹

Key Takeaways

• AI/ML and agentic tools are improving FPGA design and compilation, but downstream programming still lags.
• Higher-level languages and intelligent compilers are crucial for making FPGAs more accessible.
• AI can assist with mixed-signal co-design by optimizing DSP algorithms.
• FPGAs remain valuable for applications requiring adaptability, such as AI processing, automotive, and 5G/6G.
• Continued advancements in AI-powered tools and software abstraction are essential for unlocking the full potential of programmable logic.

¹ Fidus. “AI/ML Acceleration on Heterogeneous platforms.” https://sites.usc.edu/fpga/ai-ml/