Field-programmable gate arrays (FPGAs) provide customizable hardware that can accelerate artificial intelligence workloads. In this article, we explore how FPGAs can boost performance and energy efficiency for machine learning applications, from convolutional neural networks to recurrent architectures. We also discuss development tools and frameworks such as High-Level Synthesis (HLS) and open-source libraries that make FPGA programming more accessible to AI engineers.
FPGAs offer parallelism and flexibility unmatched by general-purpose CPUs or GPUs. By tailoring the hardware to the specific operations of neural networks, designers can achieve lower latency and power consumption. Whether you’re deploying AI at the edge or in data centers, understanding how to harness FPGA technology will open new possibilities for your projects.