{"total":57,"rows":[{"id":"2101.09671","title":"Pruning and Quantization for Deep Neural Network Acceleration: A Survey","created":"2021-01-24","cited_by_count":263,"area":null},{"id":"2305.16504","title":"On the Tool Manipulation Capability of Open-source Large Language Models","created":"2023-05-25","cited_by_count":122,"area":null},{"id":"2108.13342","title":"DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusion","created":"2021-08-30","cited_by_count":57,"area":null},{"id":"2101.12127","title":"tf.data: A Machine Learning Data Processing Framework","created":"2021-01-28","cited_by_count":41,"area":null},{"id":"2112.08429","title":"Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python","created":"2021-12-15","cited_by_count":40,"area":null},{"id":"2401.10241","title":"Zero Bubble Pipeline Parallelism","created":"2023-11-30","cited_by_count":40,"area":null},{"id":"2205.12078","title":"GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation","created":"2022-05-24","cited_by_count":13,"area":null},{"id":"2203.01877","title":"Query Processing on Tensor Computation Runtimes","created":"2022-03-03","cited_by_count":12,"area":null},{"id":"2601.07160","title":"AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units","created":"2026-01-12","cited_by_count":12,"area":null},{"id":"2311.16080","title":"XLB: A differentiable massively parallel lattice Boltzmann library in Python","created":"2023-11-27","cited_by_count":11,"area":null},{"id":"2402.06957","title":"Architectural Neural Backdoors from First Principles","created":"2024-02-10","cited_by_count":11,"area":null},{"id":"2202.09947","title":"Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation","created":"2022-02-21","cited_by_count":10,"area":null},{"id":"2301.01333","title":"oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation","created":"2023-01-03","cited_by_count":7,"area":null},{"id":"2204.09514","title":"Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems","created":"2022-04-18","cited_by_count":6,"area":null},{"id":"2403.05802","title":"UniSparse: An Intermediate Language for General Sparse Format Customization","created":"2024-03-09","cited_by_count":4,"area":null},{"id":"2110.10802","title":"A Data-Centric Optimization Framework for Machine Learning","created":"2021-10-20","cited_by_count":3,"area":null},{"id":"2203.00448","title":"Memory Planning for Deep Neural Networks","created":"2022-02-23","cited_by_count":3,"area":null},{"id":"2311.09690","title":"CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs","created":"2023-11-16","cited_by_count":3,"area":null},{"id":"2512.22147","title":"GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programs","created":"2025-12-15","cited_by_count":3,"area":null},{"id":"2601.12698","title":"A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization","created":"2026-01-19","cited_by_count":3,"area":null},{"id":"2103.14949","title":"Automated Backend-Aware Post-Training Quantization","created":"2021-03-27","cited_by_count":2,"area":null},{"id":"2302.08141","title":"Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform","created":"2023-02-16","cited_by_count":2,"area":null},{"id":"2404.09151","title":"Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging","created":"2024-04-14","cited_by_count":2,"area":null},{"id":"2602.19762","title":"Hexagon-MLIR: An AI Compilation Stack For Qualcomm's Neural Processing Units (NPUs)","created":"2026-02-23","cited_by_count":2,"area":null},{"id":"2104.04731","title":"Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programming","created":"2021-04-10","cited_by_count":1,"area":null},{"id":"2302.06751","title":"OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science","created":"2023-02-13","cited_by_count":1,"area":null},{"id":"2305.10611","title":"ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time","created":"2023-05-17","cited_by_count":1,"area":null},{"id":"2310.17408","title":"Tackling the Matrix Multiplication Micro-kernel Generation with Exo","created":"2023-10-26","cited_by_count":1,"area":null},{"id":"2406.06542","title":"vMCU: Coordinated Memory Management and Kernel Optimization for DNN Inference on MCUs","created":"2024-05-01","cited_by_count":1,"area":null},{"id":"2408.11205","title":"DSP-MLIR: A MLIR Dialect for Digital Signal Processing","created":"2024-08-20","cited_by_count":1,"area":null},{"id":"2409.02817","title":"Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators","created":"2024-09-04","cited_by_count":1,"area":null},{"id":"2505.01892","title":"DiTOX: Fault Detection and Localization in the ONNX Optimizer","created":"2025-05-03","cited_by_count":1,"area":null},{"id":"2506.14167","title":"Kolmogorov-Arnold Energy Models: Fast, Interpretable Generative Modeling","created":"2025-06-17","cited_by_count":1,"area":null},{"id":"2509.16248","title":"GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2511.08083","title":"HipKittens: Fast and Furious AMD Kernels","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2511.15323","title":"SkyEgg: Heterogeneity-Aware Hardware Synthesis via Equality Saturation","created":"2025-11-19","cited_by_count":1,"area":null},{"id":"2512.00031","title":"Hardware-Aware Neural Network Compilation with Learned Optimization: A RISC-V Accelerator Approach","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2604.14825","title":"Nautilus: An Auto-Scheduling Tensor Compiler for Efficient Tiled GPU Kernels","created":"2026-04-16","cited_by_count":1,"area":null},{"id":"2606.24780","title":"BluTrain: A C++/CUDA Framework for AI Systems","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2202.00739","title":"Compiler-Driven Simulation of Reconfigurable Hardware Accelerators","created":"2022-02-01","cited_by_count":0,"area":null},{"id":"2301.13441","title":"CMLCompiler: A Unified Compiler for Classical Machine Learning","created":"2023-01-31","cited_by_count":0,"area":null},{"id":"2306.01697","title":"Exploring Robustness of Image Recognition Models on Hardware Accelerators","created":"2023-06-02","cited_by_count":0,"area":null},{"id":"2310.10211","title":"GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation","created":"2023-10-16","cited_by_count":0,"area":null},{"id":"2401.06665","title":"PolyTOPS: Reconfigurable and Flexible Polyhedral Scheduler","created":"2024-01-12","cited_by_count":0,"area":null},{"id":"2504.11067","title":"Morphing-based Compression for Data-centric ML Pipelines","created":"2025-04-15","cited_by_count":0,"area":null},{"id":"2508.14899","title":"Accelerating GenAI Workloads by Enabling RISC-V Microkernel Support in IREE","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2509.16443","title":"LightCode: Compiling LLM Inference for Photonic-Electronic Systems","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2512.16512","title":"XTC, A Research Platform for Optimizing AI Workload Operators","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2601.11608","title":"Hardware-Aware Reformulation of Convolutions for Efficient Execution on Specialized AI Hardware: A Case Study on NVIDIA Tensor Cores","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2602.20204","title":"Analyzing Latency Hiding and Parallelism in an MLIR-based AI Kernel Compiler","created":"2026-02-22","cited_by_count":0,"area":null},{"id":"2604.07526","title":"From LLM to Silicon: RL-Driven ASIC Architecture Exploration for On-Device AI Inference","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2606.16059","title":"Mojo: A Promising Tool for Scalable Financial AI Efficiency","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2607.15865","title":"An MLIR-Based Compilation Method for Large Language Models","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2607.18254","title":"Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2608.03983","title":"Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.30104","title":"MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.36800","title":"AI as a Compiler: Compiling Triton kernels without the Triton compiler","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}