Curated data for AVR and STM32 devices

This repository contains tools for extracting data from vendor sources, filtering and reformatting them into a vendor-independent format.

This data is used by the modm project to generate its Hardware Abstraction Layer (HAL), startup code and additional support tools. Please have a look at modm's platform modules for examples on how to use this data.

These tools and this data set is maintained and curated by @salkinium only at modm-io/modm-devices. It is licensed under the MPLv2 license.

Currently data for 3780 devices is available. Please open an issue or better yet a pull request for additional support.

Family	Devices	Family	Devices	Family	Devices
AT90	12	ATMEGA	352	ATTINY	148
NRF52	7	RP20	1	SAMD	209
SAMG	10	SAML	47	SAMV	20
STM32F0	169	STM32F1	174	STM32F2	71
STM32F3	145	STM32F4	354	STM32F7	181
STM32G0	262	STM32G4	295	STM32H7	206
STM32L0	354	STM32L1	180	STM32L4	417
STM32L5	44	STM32U5	61	STM32WB	37
STM32WL	24

TL;DR

git clone https://github.com/modm-io/modm-devices.git
cd modm-devices/tools/generator
# Generate STM32 device data
make generate-stm32
# Generate SAM device data
make generate-sam
# Generate AVR device data
make generate-avr

You need Python3 with lxml, jinja2, deepdiff and CppHeaderParser packages.

pip install lxml jinja2 deepdiff CppHeaderParser

Background

The device data idea originally comes from xpcc, which is the predecessor to modm. Around 2013 we wanted to remove some of the repetitive steps for building a HAL for AVR and STM32 devices and we chose to extract some common data and collapse some peripheral drivers into Jinja2 templates.

This eventually evolved from manually extracted device data to fully generated device data as soon as we found machine readable data sources from vendors. For AVRs, we use the Atmel Target Description Files and for STM32, we use internal data extracted from the CubeMX code generator.

Thus the Device File Generator (DFG) was born. The DFG has been rewritten for modm to make it more maintainable and flexible as well as handling edge cases much better.

We've separated the device data from modm, so that it becomes easier for YOU to use this data for your own purposes. I've written an blog post with all the details.

Data quality

The quality of the resulting device data obviously depend heavily on the quality of the input data. I reluctantly maintain a manual patch list for the bugs I've encountered in the vendor sources, that I don't want to write a fix for in the DFG. I have sent some of these patches to a contact in ST, however, every new release of CubeMX changes a lot of data and can reintroduce some of these bugs. I don't have a contact at Atmel to send bug reports to.

In addition, the CubeMX and AVR data does not contain some very important information, which has to be assembled manually from hundreds of datasheets and is then injected into the DFG. This is extremely labor intensive.

Please be respectful in asking for more data: I do not like spending hours upon hours copying this additional data out of datasheets. It's also much more likely to introduce errors, so automating data extraction is much easier for me to maintain. You may of course open an issue about wrong data, but I'd prefer if you opened a pull request that fixes the problem in the DFG instead.

All fixes MUST BE REPRODUCIBLE by the DFG! This means you need to track down the bug to either the raw vendor data (=> update the manual patches) or in the DFG data pipeline (=> fix the DFG).

DO NOT UNDER ANY CIRCUMSTANCES PUBLISH THE RAW DATA EXTRACTED FROM CUBEMX ANYWHERE! It is subject to ST's copyright and you are not allowed to distribute it!

Data format

I initially wanted to format this data as device trees, however, since it is so tied to the Linux kernel, there isn't (or wasn't) much tool support available at the time (though now there is a Python parser in Zephyr), so we wrote our own tree-based format, which we called "device files" since we're so creative. It allows lossless overlaying of data trees to reduce the amount of duplicate data noise which makes it easier to comprehend as a human.

I do not intend to standardize this format, it may change at any time for any reason. This allows us maximum flexibility in encoding this complicated device information. If you want to engage in format discussions, please consider contributing to the device tree specification instead.

Since I may change this data format to accommodate new data, you should write your own formatter of this data, so that you have much better control over what your tools are expecting! So, if you need this data in the form of a Device Tree, please write your own data converter and maintain it yourself!

For modm we convert this format to a Python dictionary tree, for details see the DeviceFile class in tools/device/modm/device_file.py.