paddle-mobile将开发一个新的版本，为了：

• 支持Paddle Fluid
• 更小的体积
• 更快的速度
• 更广泛地支持各种arm设备和终端平台

因此，paddle-mobile将会进行一次完全的重构，从框架设计到代码开发。在新版本开发完成后，将会删除原有mobile-deep-learning的代码。新版本开发期间，代码将放置在paddle-mobile repo的一个子目录下。对于子目录的命名，目前有3个候选，希望大家投票：

• 👍 zygote
• 👎 paddle
• ❤️ fluid

有更好的建议，请回复issue。相关issue：#121
另外，待新版本代码开发完成，移除老版本mobile-deep-learning代码后，新版本代码将移出子目录，直接放置在paddle-mobile repo下面。

If this is not the case a page fault should be generated. Right now we generate an instruction access fault.

https://github.com/pulp-platform/ariane/blob/ad70ce1f30dad539e5a365ffe71a02aaf20b397e/src/load_store_unit.sv#L339

If you speak another language, I would appreciate your help in translating the README.md.

For tables, checklists, or other data that might change, please indicate that that information is in the main README. Otherwise every change to the main README will need to be replicated to the other READMEs.

^ I've tried to do this a bit in the French README. The only thing you need to replicate when

grep unknown opentitan.fasm  | wc
    557    6127   52651

557 bits isn't too many. Most of these are likely related to the DSP, as 1 DSP is being used:

DSP_L_X66Y110.DSP48.DSP_0.AREG_0
DSP_L_X66Y110.DSP48.DSP_0.BREG_0
DSP_L_X66Y110.DSP48.DSP_0.MASK[45:0] = 46'b1111111111111111111111111111111111111111111111
DSP_L_X66Y110.DSP48.DSP_0.ZADREG[0]
DSP_L_X66Y110.DSP48.DSP_0.ZAL

Add Javadoc to document the examples in TornadoVM.

This affects the packages under the examples module:

https://github.com/beehive-lab/TornadoVM/tree/master/examples/src/main/java/uk/ac/manchester/tornado/examples

The documentation is at the class-level and it will contain a description of how the TornadoVM API is used for each example. Additionally, it contains how to run the example

Proposed Behaviour

None of the core VPR algorithms should care if the relevant t_physical_tile is an input / output / IO type.

Current Behaviour

Some behavior is still dependent on whether a tile is an input / output / IO type.

Possible Solution

The relevant code shouldn't need to care. Once all callsites of is_input_type/is_output_type/is_io_type are removed, the

VUnit.add_source_files_from_csv() only passes vhdl_standard kwarg to Library.add_source_file(). However, my file gatherer uses .ahd file extensions to denote altera, .xhd to denote xilinx, and so on. I need the file_type available at least so I can tell VUnit that those file types are still vhdl, but all arguments should be available.

This is already possible through the API, but we should also expose it through hls4ml tool. So the user would specify his implementation of a layer, a config and function call template and the C++ code.