How are FPGAs used in AI/ML applications?

FPGAs (Field-Programmable Gate Arrays) are widely used in AI/ML (Artificial Intelligence / Machine Learning) applications due to their flexibility, parallelism, and power efficiency. Here's how they are used:

1. Custom Hardware Acceleration

FPGAs can be programmed to implement custom data paths and arithmetic units, optimizing the performance of AI/ML workloads like:

• Matrix multiplication

• Convolutions

• Activation functions

This allows them to accelerate neural network inference and even some training tasks.

2. Inference at the Edge

FPGAs are ideal for edge AI (e.g., smart cameras, IoT devices) because they:

• Consume less power than GPUs

• Offer low latency

• Can be reconfigured to support different models

Example: Real-time image recognition in autonomous drones using CNNs on FPGAs.

3. High Parallelism

FPGAs can exploit massive parallelism by:

• Executing many operations simultaneously

• Pipelining tasks to keep data flowing through hardware circuits efficiently

This suits AI workloads with large vector/matrix operations.

4. Flexibility and Customization

AI/ML models evolve rapidly. FPGAs allow:

• Rapid reprogramming to support new algorithms or data types (e.g., INT8, bfloat16)

• Custom data flow architectures that aren’t constrained by fixed GPU pipelines

5. Data Preprocessing and Postprocessing

FPGAs can handle:

• Real-time data normalization, filtering, or augmentation

• Output decoding or post-inference logic

This offloads work from the main processor and speeds up the pipeline.

6. Use in Data Centers

Companies like Microsoft (Project Brainwave) use FPGAs in servers to:

• Accelerate large-scale AI inference

• Support variable precision computing

• Scale efficiently in the cloud

Popular FPGA Platforms for AI/ML

• Xilinx (now AMD): Vitis AI, Versal AI Core

• Intel: OpenVINO with Intel FPGAs, Agilex series

• QuickLogic, Lattice: Low-power edge AI solutions

Advantages of FPGAs in AI/ML

• Lower latency than CPUs/GPUs

• Greater efficiency for custom models

• Reconfigurability for future-proofing

• Deterministic performance

Challenges

• Longer development time compared to GPUs

• Steeper learning curve (HDL, HLS, Vitis, etc.)

• Lower raw performance in training compared to GPUs/TPUs