HDLMake v3.0 release candidate: call for beta testers
We have just published the new release candidate for HDLMake and we need some help to both ensure everything is OK and contribute to future development.
Version 3.0: a major HDLMake release milestone.¶
After a massive refactoring & upgrade process, we have just taped-out the brand-new HDLMake 3.0 release candidate. This version supersedes v2.2 as the future Master candidate, as it features some new features that were planned as critical for spreading the usage of the HDLMake tool.
We apologize about the unexpected delay for this year's release... but we have devoted a lot of time in polishing and getting the source code ready for both reaching a wider audience and allowing a friendly entry to potential new contributors.
These are some of the highlighted features for the new v3.0 Release:
- Updated HDL code parser and solver: the new release includes by default the usage of an embedded HDL code parser and solver to manage the synthesis and simulation process. We have tried to be as conservative as possible to not break (so much) projects based on older versions, but please report any issue you may find.
- Support for Python 3.x: the new release supports both Python2.7 and Python3.x deployments in a single source code branch, enabling an easier integration into newer O.S. distributions.
- Native support for Linux & Windows shells: The new release not only supports Linux shells as the previous ones, but features native support too for Windows shells such as the classic CMD promt or PowerShell.
- TCL based Makefiles: in order to streamline the process of supporting as many tools as possible in a hierarchical way, in a changing world and rapidly evolving world of FPGA technology and tool providers, we have adopted TCL as the common language layer used by the generated Makefiles.
- Optional Web-based graphical frontend: A new graphical frontend based on Node.JS has been developed so that HDLMake can be friendly controlled in both local and server based deployments.
- Yocto Project Integration: the HDLMake release candidate has been successfully integrated into Yocto Project based design-flows to build an entire binary runtime for SPEC based White Rabbit deployments, ranging from the embedded Linux O.S. Kernel, libraries and drivers to the several FPGA bitstreams that need to be synthesized for dynamic hardware reconfiguration.
Call for Contributors¶
We are very interested in spreading the adoption of the HDLMake tool in as many actual deployments as possible, so do not hesitate in sending an email to the official mailing list for public requests or contacting the HDLMake active project manager for confidential or commercial issues.
Version milestone & deadline¶
Starting from this announcement, we start a Quarantine period for the HDLMake v3.0 release after we will propose to promote this version as the new Master.
In this period, in order to foster a wider adoption of the tool, GL Research will provide commercial support for free (as a free beer) to those institutions and commercial companies interested in exploring HDLMake adoption and becoming potential contributors.
In addition, we will inherit and tackle the issues from version 2.2 reported as not yet solved by their respective issuers in this period. Please, check the current status for the issue when using this release and report it to the mailing list so that we can track it.
For further information about opened issues to be tackled before the 3.0 publishing milestone, check the 3.0 version entry at OHR.
The documentation is included into the source code and written in Sphinx format. Please, report for any fix, typo or upgrade you want to be issued and solved before publishing as a stable release.
As a convenient resource, HDLMake relies on ReadTheDocs to host an updated version of the pre-built documentation for HDLMake v3.0 release candidate.
Git access to source code¶
You can find the HDLMake v3.0 release candidate source code by cloning the Git branch for release-3.0 and installing it as a common Python package on top of your favourite host machine Python deployment.