mlir-aie
heavydb
mlir-aie | heavydb | |
---|---|---|
1 | 1 | |
231 | 2,904 | |
8.7% | 0.4% | |
9.8 | 8.4 | |
4 days ago | about 2 months ago | |
MLIR | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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mlir-aie
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AMD Receives Approval for Acquisition of Xilinx
>come up with a "better" (performant, cheaper, easier to use, etc.) solution than GPUs for ML applications
you probably are aware but Xilinx themselves is attempting this with their versal aie boards which (in spirit) similar to GPUs, in that they group together a programmable fabric of programmable SIMD type compute cores.
https://www.xilinx.com/support/documentation/architecture-ma...
i have not played with one but i've been told (by a xilinx person, so grain of salt) the flow from high-level representation to that arch is more open
https://github.com/Xilinx/mlir-aie
heavydb
What are some alternatives?
torch-mlir - The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
llvm8 - Statically recompiling CHIP8 to Windows and macOS using LLVM
circt - Circuit IR Compilers and Tools
vis_avs - MinGW GCC port of Advanced Visualization Studio for Winamp
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
SVF - Static Value-Flow Analysis Framework for Source Code
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
llvm-string-obfuscator - LLVM String Obfuscator
concrete - Concrete: TFHE Compiler that converts python programs into FHE equivalent
bolt - 10x faster matrix and vector operations
llvm-bindings - LLVM bindings for Node.js/JavaScript/TypeScript
barebone-c - Eliminating the need for hand-crafted assembly in high-performance interpreters