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connectedhomeip
Matter (formerly Project CHIP) creates more connections between more objects, simplifying development for manufacturers and increasing compatibility for consumers, guided by the Connectivity Standards Alliance.
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capstone
Capstone disassembly/disassembler framework for ARM, ARM64 (ARMv8), BPF, Ethereum VM, M68K, M680X, Mips, MOS65XX, PPC, RISC-V(rv32G/rv64G), SH, Sparc, SystemZ, TMS320C64X, TriCore, Webassembly, XCore and X86.
If you're using Clang/LLVM you can also enable ML inlining[1] (assuming you build from source) which can save up to around 7% if all goes well.
There are also talks of work on just brute forcing the inlining for size problem for embedded releases for smallish applications. It's definitely feasible if the problem is important enough to you to throw some compute at it [2].
1. https://github.com/google/ml-compiler-opt
2. https://doi.org/10.1145/3503222.3507744
I’ve gotten good insight into what takes up space in binaries by profiling with Bloaty (https://github.com/google/bloaty). My last profiling session showed that clang’s ThinLTO was inlining too aggressively in some cases, causing functions that should be tiny to be 75 kB+.
Bloaty is a nice tool.
When I worked on Matter a couple years ago, we had the problem that its backend http://www.capstone-engine.org/ did not support Xtensa, and produced some Python tools that could take output from bloaty or similar data from readelf or elftools, and produce several kinds of report.
https://github.com/project-chip/connectedhomeip/blob/master/...
Bloaty is a nice tool.
When I worked on Matter a couple years ago, we had the problem that its backend http://www.capstone-engine.org/ did not support Xtensa, and produced some Python tools that could take output from bloaty or similar data from readelf or elftools, and produce several kinds of report.
https://github.com/project-chip/connectedhomeip/blob/master/...