Papers Compiler Optimization
“Compiler Optimization” 태그가 달린 논문 45편 · 필터 해제
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broa…
Code GenerationCompiler OptimizationDeep LearningCompiler Optimization for Quantum Computing Using Reinforcement Learning
Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with man…
Compiler Optimizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of moder…
Compiler OptimizationMulti-Agent Reinforcement Learning for Microprocessor Design Space Exploration
Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning architectural parameters acros…
Compiler OptimizationMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators
Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-parallel multiply-accumulate (MAC) operation…
Compiler OptimizationCPUGPULeveraging Artificial Intelligence on Binary Code Comprehension
Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, whic…
Compiler OptimizationMalware AnalysisSimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings
Function-level binary code similarity detection is a crucial aspect of cybersecurity. It enables the detection of bugs and patent infringements in released software and plays a pivotal role in preventing supply chain att…
Compiler OptimizationContrastive LearningGinex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching
Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool that can effectively serve various inference tasks on graph structured data. As the size of real-world graphs continues to scale, …
Compiler OptimizationCPUGraph Neural NetworkSecuring Optimized Code Against Power Side Channels
Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extra…
Code GenerationCompiler OptimizationCPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers have focused on either making a lightweigh…
Compiler Optimizationimage-classificationImage ClassificationModel Compression+2Learning to Combine Instructions in LLVM Compiler
Instruction combiner (IC) is a critical compiler optimization pass, which replaces a sequence of instructions with an equivalent and optimized instruction sequence at basic block level. There can be thousands of instruct…
Compiler OptimizationMachine TranslationLearning to Superoptimize Real-world Programs
Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving technique…
Compiler OptimizationImitation LearningCompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not ha…
Compiler OptimizationOpenAI GymGRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resource-constrained'' when executing large-s…
Code GenerationCompiler OptimizationAchieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facili…
2D Object Detection3D Object DetectionAutonomous VehiclesCompiler Optimization+5FOGA: Flag Optimization with Genetic Algorithm
Recently, program autotuning has become very popular especially in embedded systems, when we have limited resources such as computing power and memory where these systems run generally time-critical applications. Compile…
Compiler OptimizationPositionMLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that emb…
Compiler Optimizationreinforcement-learningReinforcement Learning (RL)Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datase…
Chinese Word SegmentationCompiler OptimizationComputational EfficiencyQuantization+1Deep Data Flow Analysis
Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of prog…
BIG-bench Machine LearningCompiler OptimizationEasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to bui…
Compiler OptimizationConversational Question AnsweringQuestion AnsweringTransfer Learning