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...
Yuriy Kashnikov, Jean Christophe Beyler, and William Jalby. 2012. Compiler optimizations: Machine Learning versus O3. In Proceedings of the 25th International Conference on Languages and Compilers for Parallel Computing (LCPC12).Y. Kashnikov, J. C. Beyler, and W. Jalby, "Compiler ...
OCEAN: An on-chip incremental-learning enhanced processor with gated recurrent neural network accelerators This paper presents an instruction and Fabric Programmable Neuron Array (iFPNA) architecture, its 28nm CMOS chip prototype, and a compiler for the accelera... C Chen,H Ding,H Peng,... - ...
XLA (Accelerated Linear Algebra) is an open-source machine learning (ML) compiler for GPUs, CPUs, and ML accelerators. The XLA compiler takes models from popular ML frameworks such as PyTorch, TensorFlow, and JAX, and optimizes them for high-performance execution across different hardware platform...
Theano - Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python. TensorFlow - Open source software library for numerical computation using data flow graphs. pomegranate - Hidden Markov Models for Python, implemented in Cython for speed and efficiency. python-tim...
Luckily, the Infer.NET compiler takes care of all that. Training All queries to the model (training, prediction and so forth) go through three steps: set priors, observe data, run inference. Both training and prediction are called “inference” because fundamentally they do the same thing: ...
Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they...
This paper presents a new approach to dramatically reduce the training time of a machine learning based compiler. This is achieved by focusing on the programs which best characterize the optimization space. By using unsupervised clustering in the program feature space we are able to dramatically ...
Proj. CDeepFuzz Paper Reading: Pytorch2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation 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...
applied_machine-learning-COMP-598-mini-project4kr**is 上传841KB 文件格式 zip Java 交通预测方法 在这个项目中,我们使用不同的分类器和预处理步骤来处理“旅行分类方法”任务。该数据集是康考迪亚大学私有 TRIP 数据集的增强版本,结合蒙特利尔 OpenData 的气态污染物数据和天气网络数据。性能比较了逻辑回归、前馈...