{"total":295,"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":"2308.06767","title":"A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations","created":"2023-08-13","cited_by_count":202,"area":null},{"id":"2106.04533","title":"Chasing Sparsity in Vision Transformers: An End-to-End Exploration","created":"2021-06-08","cited_by_count":183,"area":null},{"id":"2312.11514","title":"LLM in a flash: Efficient Large Language Model Inference with Limited Memory","created":"2023-12-12","cited_by_count":177,"area":"inference"},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93,"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":"2206.12562","title":"PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance","created":"2022-06-25","cited_by_count":79,"area":null},{"id":"2104.03438","title":"Convolutional Neural Network Pruning with Structural Redundancy Reduction","created":"2021-04-08","cited_by_count":73,"area":null},{"id":"2107.07467","title":"Only Train Once: A One-Shot Neural Network Training And Pruning Framework","created":"2021-07-15","cited_by_count":70,"area":null},{"id":"2206.10369","title":"The State of Sparse Training in Deep Reinforcement Learning","created":"2022-06-17","cited_by_count":61,"area":null},{"id":"2107.12422","title":"Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework","created":"2021-07-26","cited_by_count":52,"area":null},{"id":"2108.04657","title":"Differentiable Subset Pruning of Transformer Heads","created":"2021-08-10","cited_by_count":47,"area":null},{"id":"2101.08940","title":"Hessian-Aware Pruning and Optimal Neural Implant","created":"2021-01-22","cited_by_count":45,"area":null},{"id":"2203.08737","title":"Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey","created":"2022-03-16","cited_by_count":39,"area":null},{"id":"2209.14635","title":"Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing","created":"2022-09-29","cited_by_count":35,"area":null},{"id":"2204.11786","title":"Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications","created":"2022-04-25","cited_by_count":32,"area":null},{"id":"2405.10313","title":"How Far Are We From AGI: Are LLMs All We Need?","created":"2024-05-16","cited_by_count":31,"area":null},{"id":"2505.12079","title":"SepPrune: Structured Pruning for Efficient Deep Speech Separation","created":"2025-05-17","cited_by_count":31,"area":null},{"id":"2106.10860","title":"Multiplying Matrices Without Multiplying","created":"2021-06-21","cited_by_count":30,"area":null},{"id":"2105.09564","title":"Dual-side Sparse Tensor Core","created":"2021-05-20","cited_by_count":29,"area":null},{"id":"2106.06091","title":"DECORE: Deep Compression with Reinforcement Learning","created":"2021-06-11","cited_by_count":29,"area":null},{"id":"2203.08147","title":"Energy-Latency Attacks via Sponge Poisoning","created":"2022-03-14","cited_by_count":29,"area":null},{"id":"2205.01541","title":"Efficient Fine-Tuning of BERT Models on the Edge","created":"2022-05-03","cited_by_count":29,"area":null},{"id":"2406.12930","title":"Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization","created":"2024-06-16","cited_by_count":28,"area":null},{"id":"2202.04736","title":"Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets","created":"2022-02-09","cited_by_count":27,"area":null},{"id":"2110.09310","title":"Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention","created":"2021-10-18","cited_by_count":26,"area":null},{"id":"2206.06501","title":"Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training","created":"2022-06-13","cited_by_count":26,"area":null},{"id":"2410.14720","title":"SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models","created":"2024-10-14","cited_by_count":25,"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":"2203.03606","title":"I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization","created":"2022-03-07","cited_by_count":21,"area":null},{"id":"2209.09004","title":"EcoFormer: Energy-Saving Attention with Linear Complexity","created":"2022-09-19","cited_by_count":21,"area":null},{"id":"2105.14797","title":"RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks","created":"2021-05-31","cited_by_count":19,"area":null},{"id":"2107.07983","title":"S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration","created":"2021-07-16","cited_by_count":19,"area":null},{"id":"2310.02998","title":"ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models","created":"2023-10-04","cited_by_count":19,"area":"mllm"},{"id":"2103.05152","title":"Knowledge Evolution in Neural Networks","created":"2021-03-09","cited_by_count":18,"area":null},{"id":"2203.03996","title":"DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos","created":"2022-03-08","cited_by_count":18,"area":null},{"id":"2406.07929","title":"A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models","created":"2024-06-12","cited_by_count":18,"area":null},{"id":"2109.08957","title":"AI Accelerator Survey and Trends","created":"2021-09-18","cited_by_count":17,"area":null},{"id":"2203.07808","title":"Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs","created":"2022-03-15","cited_by_count":16,"area":null},{"id":"2207.12534","title":"Trainability Preserving Neural Pruning","created":"2022-07-25","cited_by_count":16,"area":null},{"id":"2209.06979","title":"Efficient Quantized Sparse Matrix Operations on Tensor Cores","created":"2022-09-14","cited_by_count":16,"area":null},{"id":"2106.09309","title":"Layer Folding: Neural Network Depth Reduction using Activation Linearization","created":"2021-06-17","cited_by_count":14,"area":null},{"id":"2107.12679","title":"MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN","created":"2021-07-27","cited_by_count":14,"area":null},{"id":"2203.02026","title":"Provable and Efficient Continual Representation Learning","created":"2022-03-03","cited_by_count":14,"area":null},{"id":"2206.04662","title":"DiSparse: Disentangled Sparsification for Multitask Model Compression","created":"2022-06-09","cited_by_count":14,"area":null},{"id":"2308.13998","title":"Computation-efficient Deep Learning for Computer Vision: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2505.06901","title":"Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-aware Cache Compression","created":"2025-05-11","cited_by_count":14,"area":null},{"id":"2101.07948","title":"SparseDNN: Fast Sparse Deep Learning Inference on CPUs","created":"2021-01-20","cited_by_count":13,"area":null},{"id":"2111.06705","title":"A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning","created":"2021-11-11","cited_by_count":13,"area":null},{"id":"2112.09245","title":"Automated Deep Learning: Neural Architecture Search Is Not the End","created":"2021-12-16","cited_by_count":13,"area":null},{"id":"2302.04852","title":"SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks","created":"2023-02-09","cited_by_count":13,"area":null},{"id":"2303.07677","title":"SR-init: An interpretable layer pruning method","created":"2023-03-14","cited_by_count":13,"area":null},{"id":"2106.16006","title":"Improving the Efficiency of Transformers for Resource-Constrained Devices","created":"2021-06-30","cited_by_count":12,"area":null},{"id":"2109.14960","title":"Prune Your Model Before Distill It","created":"2021-09-30","cited_by_count":12,"area":"distill"},{"id":"2205.08099","title":"Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey","created":"2022-05-17","cited_by_count":12,"area":null},{"id":"2209.00606","title":"Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation","created":"2022-09-01","cited_by_count":12,"area":null},{"id":"2210.10737","title":"RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations","created":"2022-10-19","cited_by_count":12,"area":null},{"id":"2103.14739","title":"Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses","created":"2021-03-26","cited_by_count":11,"area":null},{"id":"2110.13220","title":"Demystifying and Generalizing BinaryConnect","created":"2021-10-25","cited_by_count":11,"area":null},{"id":"2205.04702","title":"Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards","created":"2022-05-10","cited_by_count":11,"area":null},{"id":"2403.07094","title":"FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning","created":"2024-03-11","cited_by_count":11,"area":null},{"id":"2510.14557","title":"MX+: Pushing the Limits of Microscaling Formats for Efficient Large Language Model Serving","created":"2025-10-16","cited_by_count":11,"area":"inference"},{"id":"2104.04241","title":"Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key","created":"2021-04-09","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":"2109.01262","title":"On the Accuracy of Analog Neural Network Inference Accelerators","created":"2021-09-03","cited_by_count":10,"area":null},{"id":"2309.06626","title":"Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity","created":"2023-09-12","cited_by_count":10,"area":null},{"id":"2411.01800","title":"Expanding Sparse Tuning for Low Memory Usage","created":"2024-11-04","cited_by_count":10,"area":null},{"id":"2105.14070","title":"Accelerating Neural ODEs Using Model Order Reduction","created":"2021-05-28","cited_by_count":9,"area":null},{"id":"2112.15131","title":"Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques","created":"2021-12-30","cited_by_count":9,"area":null},{"id":"2403.19490","title":"Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment","created":"2024-03-28","cited_by_count":9,"area":null},{"id":"2501.10658","title":"LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator","created":"2025-01-18","cited_by_count":9,"area":null},{"id":"2105.08820","title":"RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance","created":"2021-05-18","cited_by_count":8,"area":null},{"id":"2204.03227","title":"Accelerating Attention through Gradient-Based Learned Runtime Pruning","created":"2022-04-07","cited_by_count":8,"area":null},{"id":"2208.12816","title":"Complexity-Driven CNN Compression for Resource-constrained Edge AI","created":"2022-08-26","cited_by_count":8,"area":null},{"id":"2210.00497","title":"Approximate Computing and the Efficient Machine Learning Expedition","created":"2022-10-02","cited_by_count":8,"area":null},{"id":"2309.06973","title":"DNNShifter: An Efficient DNN Pruning System for Edge Computing","created":"2023-09-13","cited_by_count":8,"area":null},{"id":"2402.01089","title":"No Free Prune: Information-Theoretic Barriers to Pruning at Initialization","created":"2024-02-02","cited_by_count":8,"area":null},{"id":"2407.14073","title":"LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks","created":"2024-07-19","cited_by_count":8,"area":null},{"id":"2510.08464","title":"Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recovered","created":"2025-10-09","cited_by_count":8,"area":"vla"},{"id":"2102.06408","title":"Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors","created":"2021-02-12","cited_by_count":7,"area":null},{"id":"2105.15035","title":"Machine Learning for Security in Vehicular Networks: A Comprehensive Survey","created":"2021-05-31","cited_by_count":7,"area":null},{"id":"2310.10054","title":"NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models","created":"2023-10-16","cited_by_count":7,"area":null},{"id":"2403.18587","title":"The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision","created":"2024-03-27","cited_by_count":7,"area":null},{"id":"2408.16772","title":"An Effective Information Theoretic Framework for Channel Pruning","created":"2024-08-14","cited_by_count":7,"area":null},{"id":"2104.09252","title":"Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors","created":"2021-04-19","cited_by_count":6,"area":null},{"id":"2107.12673","title":"COPS: Controlled Pruning Before Training Starts","created":"2021-07-27","cited_by_count":6,"area":null},{"id":"2304.00245","title":"Reusing Deep Neural Network Models through Model Re-engineering","created":"2023-04-01","cited_by_count":6,"area":null},{"id":"2312.02923","title":"MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning","created":"2023-12-05","cited_by_count":6,"area":null},{"id":"2409.17092","title":"Accumulator-Aware Post-Training Quantization for Large Language Models","created":"2024-09-25","cited_by_count":6,"area":null},{"id":"2104.07329","title":"All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks","created":"2021-04-15","cited_by_count":5,"area":null},{"id":"2105.03608","title":"Optimising Resource Management for Embedded Machine Learning","created":"2021-05-08","cited_by_count":5,"area":null},{"id":"2107.06825","title":"A Generalized Lottery Ticket Hypothesis","created":"2021-07-03","cited_by_count":5,"area":null},{"id":"2107.06916","title":"Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion","created":"2021-07-14","cited_by_count":5,"area":null},{"id":"2109.12550","title":"MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers","created":"2021-09-26","cited_by_count":5,"area":null},{"id":"2203.00158","title":"GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks","created":"2022-03-01","cited_by_count":5,"area":null},{"id":"2203.11540","title":"Scale-out Systolic Arrays","created":"2022-03-22","cited_by_count":5,"area":null},{"id":"2209.08938","title":"Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud","created":"2022-09-19","cited_by_count":5,"area":null},{"id":"2210.09887","title":"MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos","created":"2022-10-18","cited_by_count":5,"area":null},{"id":"2301.10388","title":"SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators","created":"2023-01-25","cited_by_count":5,"area":null},{"id":"2303.13635","title":"Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training","created":"2023-03-22","cited_by_count":5,"area":null}],"next_cursor":"100"}