Lilly Tech Systems
FPGA for AI
Field-Programmable Gate Arrays are reconfigurable chips that can be rewired after manufacturing. For AI, they offer a middle ground between the flexibility of GPUs and the efficiency of ASICs — with unique advantages in latency, power, and custom precision.
What Is an FPGA?
An FPGA is an array of configurable logic blocks, DSP slices, and memory blocks connected by a programmable routing fabric. Unlike a CPU or GPU with fixed circuits, an FPGA can be reconfigured to implement any digital circuit:
- Look-Up Tables (LUTs): Implement arbitrary logic functions
- DSP Slices: Hardened multiply-accumulate units for math-heavy operations
- Block RAM: Distributed on-chip memory for data caching
- I/O blocks: High-speed interfaces to external memory and networks
- Routing fabric: Programmable interconnects between all components
FPGA Advantages for AI
| Advantage | Why It Matters |
|---|---|
| Low latency | Custom pipelines process data with microsecond latency, critical for real-time applications |
| Custom precision | Implement any bit width (e.g., 3-bit, 6-bit) not just standard INT8/FP16 |
| Power efficiency | Better performance per watt than GPUs for inference workloads |
| Reconfigurability | Update the hardware design to support new model architectures without new silicon |
| Deterministic timing | Guaranteed worst-case latency, important for safety-critical applications |
| No NRE costs | No $100M+ mask costs like ASICs. Iterate on designs without fabrication |
Design Approaches
High-Level Synthesis (HLS)
Write AI accelerator logic in C/C++ and let HLS tools (Vitis HLS, Intel HLS) convert it to hardware. Fastest path to working FPGA designs, though less optimal than hand-coded RTL.
RTL Design
Write Verilog or VHDL directly for maximum control over timing, area, and power. Used for production deployments where every cycle counts.
Overlay Architectures
Pre-built, configurable neural network accelerator templates that sit on the FPGA. Load different model configurations without re-synthesizing the hardware.
Framework Tools
Tools like Vitis AI (AMD/Xilinx) and OpenVINO (Intel) compile trained models directly to FPGA bitstreams with minimal hardware knowledge required.
FPGA Platforms for AI
| Platform | Vendor | Target | AI Framework |
|---|---|---|---|
| Versal AI Core | AMD/Xilinx | Data center inference | Vitis AI |
| Stratix 10 NX | Intel/Altera | AI inference, HPC | OpenVINO |
| Kria SOM | AMD/Xilinx | Edge AI | Vitis AI, PYNQ |
| Agilex | Intel/Altera | Data center, edge | OpenVINO |
When to Choose FPGA
- Ultra-low latency: Financial trading, autonomous driving, real-time video processing
- Edge deployment: Limited power budget, no cloud connectivity, small form factor
- Custom precision: Quantization research, non-standard numeric formats
- ASIC prototyping: Validate chip designs on FPGA before committing to fabrication
- Low-to-medium volume: Not enough volume to justify ASIC costs, but need more efficiency than GPUs