Talks - Tim Paine: Building FPGA-based Machine Learning Accelerators in Python

FPGAs (Field Programmable Gate Arrays) can now be programmed entirely using Python tools, making hardware development more accessible to software developers

Entry-level FPGA development boards are available for under $200, with educational discounts making them even more affordable

Key open source tools like Litex, Amaranth, and Brevitas enable Python-based hardware development workflows without requiring hardware description languages like Verilog

Machine learning models can be deployed on FPGAs using Python frameworks, with capabilities to handle quantization and optimization for hardware

Modern FPGAs often come as Systems on Chip (SoC) combining ARM processors with FPGA fabric, allowing mixed software/hardware development

The open source hardware ecosystem has evolved significantly in the past 10 years, with many formerly proprietary tools now having open source alternatives

PCIe-based FPGA boards can achieve significant performance for ML inference, with examples showing 133,000 images/second throughput

Companies like Xilinx (AMD), Intel/Altera, and Lattice provide development tools that increasingly support Python-based workflows

Hardware verification and testing can be done using Python frameworks like CocoaTB

The barrier to entry for FPGA development has decreased substantially, though still requires more domain knowledge than pure software development