The Cortex-M1 is an ARM processor designed for embedded and IoT applications. It is part of the Cortex-M series of processors, which are known for their low power consumption, real-time capabilities, and ease of programming. Integrating the Cortex-M1 into the Pynq framework allows developers to take advantage of the flexibility and productivity of Python while still using a powerful and efficient microcontroller.
Overview of the Cortex-M1
The Cortex-M1 is a 32-bit RISC processor optimized for embedded systems. Key features include:
- Max clock speed of 150MHz
- Thumb-2 instruction set for improved code density
- Built-in Nested Vectored Interrupt Controller (NVIC)
- Memory Protection Unit (MPU) for security
- Low power modes to extend battery life
The Cortex-M1 is designed to be integrated with custom logic in an SoC or FPGA. It has a streamlined architecture with minimal power and area requirements. This makes it well-suited for applications like IoT edge nodes, industrial control, and robotics.
Pynq framework overview
The Pynq framework from Xilinx allows developers to use Python and Jupyter notebooks to program FPGA boards like the Zynq. Key elements of Pynq include:
- Python API to manage hardware peripherals and control logic
- Jupyter notebook environment for development
- IP catalog of common peripherals and overlays
- BFM simulation models for pre-silicon development
- Reference designs and tutorials
Pynq lowers the barrier to using FPGAs by abstracting away much of the hardware details. Developers can focus on their application logic rather than writing Verilog or VHDL.
Integrating the Cortex-M1 into the Pynq framework
To integrate the Cortex-M1 into Pynq, both hardware and software changes are required.
Hardware
On the hardware side, a custom overlay must be created that instantiates the Cortex-M1 along with any necessary peripherals, interconnect, and bridges. Some key considerations:
- Selecting the right IP blocks for peripherals like GPIO, timers, communication interfaces etc.
- Interfacing the processor with DDR memory or other memory architecture
- Bridging between the PS and PL using AXI or ACE interfaces
- Managing interrupts and interrupt routing
- Implementing an external configuration interface like JTAG
- Adding any acceleration logic or custom IP
The Vivado IP catalog provides many of the necessary IPs. The processor, interconnect, peripherals, and bridges can be stitched together in the block design tool. Simulation models are critical for verifying the overlay pre-silicon.
Software
On the software side, drivers need to be written for the Cortex-M1 and any custom IP in the overlay. The Pynq Python API can be extended with these drivers to allow control over the new hardware from Jupyter notebooks.
Some key software development steps include:
- Creating a Board Support Package (BSP) for the Cortex-M1 with startup code and linker scripts
- Writing HAL and device drivers for the processor’s peripherals and any custom IP
- Extending the Pynq Microblaze Python API with the new drivers
- Updating the Jupyter notebook UI to provide access to the new functionality
- Porting any necessary libraries, RTOSes, or other firmware code
Software can be incrementally tested on the BFM simulation models prior to overlay fabrication. This allows validation of the Python interface and drivers before silicon.
Programming the Cortex-M1 from Jupyter notebooks
After integrating the Cortex-M1 into Pynq, developers can leverage the interactive Python environment to configure the processor and accelerate application development.
From a Jupyter notebook, users can:
- Initialize and configure the Cortex-M1 by writing to control registers
- Allocate and transfer data between the PS and Cortex-M1 memory spaces
- Control peripherals like GPIO, timers, and communication blocks
- Monitor interrupts and external events
- Load overlay or application-specific firmware onto the Cortex-M1
- Pass data and synchronize processing between the PS and Cortex-M1
All these tasks can be scripted in Python, allowing for rapid testing and iteration. The interactive notebook provides visibility into the hardware operation.
For more complex firmware, the Cortex-M1 code can still be developed in C/C++ tools like ARM Keil MDK or IAR EWARM. The firmware can be integrated into the Python environment using the driver interface.
Example integration: MQTT network coprocessor
As a concrete example, the Cortex-M1 could be integrated into Pynq as an MQTT network coprocessor.
The overlay would contain:
- Cortex-M1 processor
- External DDR memory for message storage
- Ethernet MAC peripheral for connectivity
- AXI interconnect and bridges
- Interrupt controller
An MQTT protocol stack would run on the Cortex-M1 firmware. Python notebook code would interface with the firmware driver to:
- Initialize MQTT client parameters
- Publish messages to topics
- Subscribe to topics and receive published messages
- Monitor network events
This offloads the network stack from the PS to the more efficient Cortex-M1 while still providing simple Python access. The Cortex-M1 integration enables real-time MQTT performance without taxing PS resources.
Conclusion
Integrating the Cortex-M1 into the Pynq framework combines the efficiency of an embedded ARM processor with the rapid development capabilities of Python and Jupyter notebooks. With careful hardware and software co-design, the Cortex-M1 can be deployed as either a standalone accelerator or a more capable network coprocessor while still being programmed seamlessly from Python. This allows developers to leverage the benefits of both worlds to meet the needs of sophisticated embedded and IoT applications.
