{"total":76,"rows":[{"id":"2106.08962","title":"Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better","created":"2021-06-16","cited_by_count":154,"area":null},{"id":"2302.14017","title":"Full Stack Optimization of Transformer Inference: a Survey","created":"2023-02-27","cited_by_count":80,"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":"2207.04296","title":"TensorIR: An Abstraction for Automatic Tensorized Program Optimization","created":"2022-07-09","cited_by_count":38,"area":null},{"id":"2410.20399","title":"ThunderKittens: Simple, Fast, and Adorable AI Kernels","created":"2024-10-27","cited_by_count":36,"area":null},{"id":"2104.04955","title":"A Deep Learning Based Cost Model for Automatic Code Optimization","created":"2021-04-11","cited_by_count":33,"area":null},{"id":"2205.13603","title":"Tensor Program Optimization with Probabilistic Programs","created":"2022-05-26","cited_by_count":30,"area":null},{"id":"2210.09603","title":"Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs","created":"2022-10-18","cited_by_count":28,"area":null},{"id":"2103.01489","title":"Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search","created":"2021-03-02","cited_by_count":24,"area":null},{"id":"2105.01898","title":"CoSA: Scheduling by Constrained Optimization for Spatial Accelerators","created":"2021-05-05","cited_by_count":22,"area":null},{"id":"2110.10221","title":"The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding","created":"2021-10-19","cited_by_count":17,"area":null},{"id":"2201.09736","title":"Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning","created":"2022-01-21","cited_by_count":16,"area":null},{"id":"2308.13490","title":"TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs","created":"2023-08-25","cited_by_count":16,"area":null},{"id":"2404.04815","title":"Allo: A Programming Model for Composable Accelerator Design","created":"2024-04-07","cited_by_count":16,"area":null},{"id":"2104.13997","title":"MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores","created":"2021-04-28","cited_by_count":15,"area":null},{"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":"2101.08458","title":"UNIT: Unifying Tensorized Instruction Compilation","created":"2021-01-21","cited_by_count":10,"area":null},{"id":"2105.01585","title":"HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation","created":"2021-05-04","cited_by_count":10,"area":null},{"id":"2104.05755","title":"Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads","created":"2021-04-12","cited_by_count":9,"area":null},{"id":"2301.13062","title":"Operator Fusion in XLA: Analysis and Evaluation","created":"2023-01-30","cited_by_count":9,"area":null},{"id":"2312.09708","title":"GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy","created":"2023-12-15","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":"2102.02344","title":"Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models","created":"2021-02-03","cited_by_count":7,"area":null},{"id":"2210.01075","title":"Decompiling x86 Deep Neural Network Executables","created":"2022-10-03","cited_by_count":7,"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":"2403.11522","title":"LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers","created":"2024-03-18","cited_by_count":7,"area":null},{"id":"2103.06376","title":"Functional Collection Programming with Semi-Ring Dictionaries","created":"2021-03-10","cited_by_count":6,"area":null},{"id":"2104.13732","title":"A Reinforcement Learning Environment for Polyhedral Optimizations","created":"2021-04-28","cited_by_count":5,"area":null},{"id":"2105.03215","title":"Bring Your Own Codegen to Deep Learning Compiler","created":"2021-05-03","cited_by_count":5,"area":null},{"id":"2304.12576","title":"Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures","created":"2023-04-25","cited_by_count":5,"area":null},{"id":"2407.16847","title":"SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention","created":"2024-07-23","cited_by_count":5,"area":null},{"id":"2408.04808","title":"Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10","created":"2024-08-09","cited_by_count":5,"area":null},{"id":"2506.18927","title":"From Tiny Machine Learning to Tiny Deep Learning: A Survey","created":"2025-06-21","cited_by_count":5,"area":null},{"id":"2205.03960","title":"Neural Architecture Search using Property Guided Synthesis","created":"2022-05-08","cited_by_count":4,"area":null},{"id":"2402.02953","title":"Unraveling the Key of Machine Learning-based Android Malware Detection","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2403.00176","title":"SoD$^2$: Statically Optimizing Dynamic Deep Neural Network","created":"2024-02-29","cited_by_count":4,"area":null},{"id":"2407.08192","title":"Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance","created":"2024-07-11","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":"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":"2504.12984","title":"Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation","created":"2025-04-17","cited_by_count":3,"area":null},{"id":"2604.06425","title":"Neural Computers","created":"2026-04-07","cited_by_count":3,"area":null},{"id":"2606.27153","title":"DMuon: Efficient Distributed Muon Training with Near-Adam Overhead","created":"2026-06-25","cited_by_count":3,"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":"2210.03731","title":"Demystifying Map Space Exploration for NPUs","created":"2022-10-07","cited_by_count":2,"area":null},{"id":"2210.13086","title":"Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2309.01226","title":"Saturn: An Optimized Data System for Large Model Deep Learning Workloads","created":"2023-09-03","cited_by_count":2,"area":null},{"id":"2310.04696","title":"Serving Deep Learning Model in Relational Databases","created":"2023-10-07","cited_by_count":2,"area":null},{"id":"2311.18174","title":"Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving","created":"2023-11-30","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":"2212.01005","title":"AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization","created":"2022-12-02","cited_by_count":1,"area":null},{"id":"2307.12234","title":"MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems","created":"2023-07-23","cited_by_count":1,"area":null},{"id":"2308.12419","title":"Toward American Sign Language Processing in the Real World: Data, Tasks, and Methods","created":"2023-08-23","cited_by_count":1,"area":null},{"id":"2308.14364","title":"Target-independent XLA optimization using Reinforcement Learning","created":"2023-08-28","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":"2310.00574","title":"YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2401.03384","title":"conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks","created":"2024-01-07","cited_by_count":1,"area":null},{"id":"2405.16883","title":"Scorch: A Library for Sparse Deep Learning","created":"2024-05-27","cited_by_count":1,"area":null},{"id":"2410.03210","title":"Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2501.05408","title":"Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphs","created":"2025-01-09","cited_by_count":1,"area":null},{"id":"2511.10480","title":"Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs","created":"2025-11-13","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":"2305.08872","title":"AMULET: Adaptive Matrix-Multiplication-Like Tasks","created":"2023-05-12","cited_by_count":0,"area":null},{"id":"2311.17815","title":"A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures","created":"2023-11-29","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":"2405.16623","title":"Graph neural networks with configuration cross-attention for tensor compilers","created":"2024-05-26","cited_by_count":0,"area":null},{"id":"2407.13726","title":"Compressing Structured Tensor Algebra","created":"2024-07-18","cited_by_count":0,"area":null},{"id":"2509.20020","title":"The Syntax and Semantics of einsum","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2512.01678","title":"Morphling: Fast, Fused, and Flexible GNN Training at Scale","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2601.16294","title":"Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple","created":"2026-01-22","cited_by_count":0,"area":null},{"id":"2603.06731","title":"PolyBlocks: A Compiler Infrastructure for AI Chips and Programming Frameworks","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2607.27987","title":"It's All Just Vectorization: einx, a Universal Notation for Tensor Operations","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.00029","title":"Nova: An End-to-End MLIR Compiler for Deep Learning","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.21555","title":"Tensor Seeks Layout: Formalizing Layout Selection for ML Compilers","created":"2026-08-21","cited_by_count":0,"area":null}],"next_cursor":null}