参考论文Machine Learning in Compiler Optimization I. Introduction It is All About Optimization 编译器有两个任务:translation和optimization。translation是成功将程序翻译成可执行文件。optimization是找到最高效的翻译。 在之前,编译和机器学习是两个不交叠的领域,现在这两个领域结合在了一起。因为可以把代码看做一...
The number of optimizations that are available in modern day compilers are in their hundreds, and would only grow in number in the future. This increase in the number of optimizations available to the compiler is primarily due to the fact that each optimization would try and target specific ...
Compiler Auto-Vectorization with Imitation Learning - Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin. NeurIPS 2019. Multi-objective Exploration for Practical Optimization Decisions in Binary Translation - Sunghyun Park, Youfeng Wu, Janghaeng Lee, Amir Aupov, and Scot...
The wealth of available compiler optimizations leads to the dual problems of finding the best set of optimizations and the best heuris-tic parameters to tune each optimization. We describe how machine learning techniques, such as logistic regression, can be used to address these problems. We ...
quantum-algorithmsquantum-machine-learningquantum-compilerquantum-optimizationquantum-applicationsquantum-softwarequantum-open-sourcequantum-synthesis UpdatedJan 29, 2025 Jupyter Notebook A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum...
The eIQ Glow neural network compiler software for i.MX RT devices that is found in the MCUXPresso SDK package can be ported to other microcontroller devices in the RT family as well as to some LPC and Kinetis devices. Glow supports compiling machine learning models for Cortex-M4, Cortex-M7,...
(input_fld, weight_file) K.set_learning_phase(0) net_model = load_model(weight_file_path) pred = [None]*num_output pred_node_names = [None]*num_output for i in range(num_output): pred_node_names[i] = prefix_output_node_names_of_final_network+str(i) pred[i] = ...
Abstract 工具:TorchDynamo, TorchInductor Task: 实现了torch.compile的解释器和编译器 TorchDynamo: Task: Python-level just-in-time(JIT) compiler Method: 允许graph compi
Chen, X., Tian, Y.: Learning to perform local rewriting for combinatorial optimization. In: International Conference on Neural Information Processing Systems (NeurIPS) (2019) Google Scholar Cheng, L., Wong, M.D.: Floorplan design for multimillion gate FPGAs. IEEE Trans. Comput. Aided Design...
Finally, we leveraged an ANE compiler optimization that splits the computation of layers with large spatial dimensions into small spatial tiles, and makes a trade-off between latency and memory usage. Together, these techniques yielded an extreme reduction in the memory footprint of our model and...