{"total":23,"rows":[{"id":"2211.03578","title":"TLP: A Deep Learning-based Cost Model for Tensor Program Tuning","created":"2022-11-07","cited_by_count":14,"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":"2205.10364","title":"Learning to Reverse DNNs from AI Programs Automatically","created":"2022-05-20","cited_by_count":12,"area":null},{"id":"2307.04963","title":"DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization","created":"2023-07-11","cited_by_count":9,"area":null},{"id":"2102.06599","title":"Neural Architecture Search as Program Transformation Exploration","created":"2021-02-12","cited_by_count":8,"area":null},{"id":"2103.16139","title":"Enabling Homomorphically Encrypted Inference for Large DNN Models","created":"2021-03-30","cited_by_count":6,"area":null},{"id":"2202.05048","title":"Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment","created":"2022-02-10","cited_by_count":6,"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":"2202.09888","title":"Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch","created":"2022-02-20","cited_by_count":3,"area":null},{"id":"2401.12428","title":"CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators","created":"2024-01-23","cited_by_count":3,"area":null},{"id":"2101.06371","title":"NNStreamer: Efficient and Agile Development of On-Device AI Systems","created":"2021-01-16","cited_by_count":2,"area":null},{"id":"2111.00655","title":"Collage: Seamless Integration of Deep Learning Backends with Automatic Placement","created":"2021-11-01","cited_by_count":2,"area":null},{"id":"2104.04576","title":"Compiler Toolchains for Deep Learning Workloads on Embedded Platforms","created":"2021-03-08","cited_by_count":1,"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":"2309.01825","title":"LoopTune: Optimizing Tensor Computations with Reinforcement Learning","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2404.12512","title":"Proteus: Preserving Model Confidentiality during Graph Optimizations","created":"2024-04-18","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":"2201.00194","title":"FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity","created":"2022-01-01","cited_by_count":0,"area":null},{"id":"2210.12415","title":"ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations","created":"2022-10-22","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":"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":"2610.15808","title":"Grounding Deep Learning Framework Testing in Data Flow","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}