PyXL Promises Orders of Magnitude Faster Embedded Python — Using a Python-Native Custom Processor

The PyXL project is aiming to boost the performance of Python for embedded projects, promising a thirtyfold speed increase over MicroPython — by using a custom Python-specific processor, currently proven in FPGA form.

"Python is powerful but slow — held back by interpreter overhead and dynamic typing," the project's creator Ron Livne claims. "What if it ran natively on hardware? I built PyXL, a custom processor designed to accelerate Python execution in silicon, eliminating its biggest performance bottlenecks. PyXL achieves massive efficiency gains per cycle, even significantly surpassing high-end CPUs like the [Apple] M1 Pro!"

Python is a popular language thanks to its accessibility, but despite heavy use in high-performance computing circles is considerably less efficient than languages like C/C++ and direct assembler. The traditional way around this is to farm the heavy computational load off to a library written in a faster language — but Livne's project takes the opposite approach, and changes the hardware on which Python runs instead.

"PyXL is a custom hardware processor that executes Python directly — no interpreter, no JIT [Just-In-Time compilation], and no tricks. It takes regular Python code and runs it in silicon," software engineer Livne explains. "A custom toolchain compiles a .py file into CPython ByteCode, translates it to a custom assembly, and produces a binary that runs on a pipelined processor built from scratch."

The results are impressive: a program written to measure the round-trip latency for toggling a general-purpose input/output (GPIO) pin shows a thirtyfold gain when using a PyXL processor implemented on a Digilent Arty FPGA development board than when using MicroPython with the general-purpose processor on a PyBoard development board — increasing to fiftyfold when you account for the difference in clock speed between the two.

"This isn’t just a performance boost — it's an unlock. PyXL brings a level of responsiveness and determinism that Python has never had in embedded or real-time contexts," Livne claims. "Python VMs [Virtual Machines] — even those designed for microcontrollers — are still built around software interpreters. That introduces overhead and complexity between your code and the hardware. PyXL removes this barrier. Your Python code is executed directly in hardware. GPIO access is physical. Control flow is predictable. Execution is tight and consistent by design."

This, Livne says, makes Python better suited to use-cases including real-time control systems, machine learning and artificial intelligence (ML and AI) inference and sensor response loops, robotics tasks including motor feedback and sensor fusion at a cycle-precise level, and in embedded industrial systems "where timing and reliability matter."

More information is available on the PyXL website, though code has yet to be released; Livne is scheduled to give a presentation on the project at PyCon 2025 on Saturday, May 17th in Pitsburgh, Pennsylvania.