{"total":90,"rows":[{"id":"2406.16838","title":"From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models","created":"2024-06-24","cited_by_count":137,"area":null},{"id":"2407.18921","title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","created":"2024-07-09","cited_by_count":128,"area":null},{"id":"2409.15790","title":"Small Language Models: Survey, Measurements, and Insights","created":"2024-09-24","cited_by_count":112,"area":null},{"id":"2409.00088","title":"On-Device Language Models: A Comprehensive Review","created":"2024-08-26","cited_by_count":104,"area":null},{"id":"2406.06282","title":"PowerInfer-2: Fast Large Language Model Inference on a Smartphone","created":"2024-06-10","cited_by_count":103,"area":null},{"id":"2402.03804","title":"ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs","created":"2024-02-06","cited_by_count":67,"area":null},{"id":"2402.09748","title":"Model Compression and Efficient Inference for Large Language Models: A Survey","created":"2024-02-15","cited_by_count":64,"area":null},{"id":"2407.05858","title":"Fast On-device LLM Inference with NPUs","created":"2024-07-08","cited_by_count":62,"area":null},{"id":"2410.18038","title":"POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference","created":"2024-10-23","cited_by_count":47,"area":null},{"id":"2412.04315","title":"Densing Law of LLMs","created":"2024-12-05","cited_by_count":40,"area":null},{"id":"2410.07265","title":"A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models","created":"2024-10-08","cited_by_count":23,"area":null},{"id":"2409.02877","title":"Configurable Foundation Models: Building LLMs from a Modular Perspective","created":"2024-09-04","cited_by_count":22,"area":null},{"id":"2411.02335","title":"Sparsing Law: Towards Large Language Models with Greater Activation Sparsity","created":"2024-11-04","cited_by_count":22,"area":null},{"id":"2502.11880","title":"Bitnet.cpp: Efficient Edge Inference for Ternary LLMs","created":"2025-02-17","cited_by_count":22,"area":null},{"id":"2402.01799","title":"Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward","created":"2024-02-02","cited_by_count":21,"area":null},{"id":"2410.04691","title":"Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning","created":"2024-10-07","cited_by_count":21,"area":null},{"id":"2411.16102","title":"BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching","created":"2024-11-25","cited_by_count":19,"area":null},{"id":"2504.08784","title":"SLOs-Serve: Optimized Serving of Multi-SLO LLMs","created":"2025-04-05","cited_by_count":19,"area":null},{"id":"2410.18311","title":"CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation","created":"2024-10-23","cited_by_count":14,"area":null},{"id":"2502.15618","title":"Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing","created":"2025-02-21","cited_by_count":14,"area":null},{"id":"2511.07885","title":"Intelligence per Watt: Measuring Intelligence Efficiency of Local AI","created":"2025-11-11","cited_by_count":14,"area":null},{"id":"2501.01990","title":"Towards Sustainable Large Language Model Serving","created":"2024-12-31","cited_by_count":12,"area":null},{"id":"2506.16500","title":"SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity","created":"2025-06-19","cited_by_count":9,"area":null},{"id":"2403.11621","title":"Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model","created":"2024-03-18","cited_by_count":8,"area":null},{"id":"2409.01366","title":"CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification","created":"2024-09-02","cited_by_count":7,"area":null},{"id":"2411.05046","title":"PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2412.20185","title":"DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization","created":"2024-12-28","cited_by_count":7,"area":null},{"id":"2408.15518","title":"Squid: Long Context as a New Modality for Energy-Efficient On-Device Language Models","created":"2024-08-28","cited_by_count":6,"area":null},{"id":"2505.14884","title":"Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity","created":"2025-05-20","cited_by_count":6,"area":null},{"id":"2507.01299","title":"La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation","created":"2025-07-02","cited_by_count":6,"area":null},{"id":"2509.22075","title":"CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning","created":"2025-09-26","cited_by_count":6,"area":null},{"id":"2406.01392","title":"Sparsity-Accelerated Training for Large Language Models","created":"2024-06-03","cited_by_count":5,"area":null},{"id":"2410.19274","title":"Neuralink: Fast LLM Inference on Smartphones with Neuron Co-Activation Linking","created":"2024-10-25","cited_by_count":5,"area":null},{"id":"2412.07174","title":"Post-Training Statistical Calibration for Higher Activation Sparsity","created":"2024-12-10","cited_by_count":5,"area":null},{"id":"2504.16397","title":"Compass: SLO-aware Query Planner for Compound AI Serving at Scale","created":"2025-04-23","cited_by_count":5,"area":null},{"id":"2506.06644","title":"Spark Transformer: Reactivating Sparsity in FFN and Attention","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2406.06562","title":"Achieving Sparse Activation in Small Language Models","created":"2024-06-03","cited_by_count":4,"area":null},{"id":"2409.03856","title":"Sirius: Contextual Sparsity with Correction for Efficient LLMs","created":"2024-09-05","cited_by_count":4,"area":null},{"id":"2409.04833","title":"Achieving Peak Performance for Large Language Models: A Systematic Review","created":"2024-09-07","cited_by_count":4,"area":null},{"id":"2503.03777","title":"FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference","created":"2025-03-04","cited_by_count":4,"area":null},{"id":"2503.12167","title":"PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing","created":"2025-03-15","cited_by_count":4,"area":null},{"id":"2504.08378","title":"Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash","created":"2025-04-11","cited_by_count":4,"area":null},{"id":"2505.07239","title":"Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity","created":"2025-05-12","cited_by_count":4,"area":null},{"id":"2507.03153","title":"HGCA: Hybrid GPU-CPU Attention for Long Context LLM Inference","created":"2025-07-03","cited_by_count":4,"area":null},{"id":"2409.09071","title":"Elastic On-Device LLM Service","created":"2024-09-08","cited_by_count":3,"area":null},{"id":"2410.14740","title":"Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2505.06461","title":"Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference","created":"2025-05-09","cited_by_count":3,"area":null},{"id":"2407.05690","title":"Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2412.05644","title":"Mixture of Hidden-Dimensions Transformer","created":"2024-12-07","cited_by_count":2,"area":null},{"id":"2501.05465","title":"Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)","created":"2025-01-03","cited_by_count":2,"area":null},{"id":"2502.11417","title":"DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2510.15964","title":"Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity","created":"2025-10-12","cited_by_count":2,"area":null},{"id":"2606.23001","title":"EnerInfer: Energy-Aware On-Device LLM Inference","created":"2026-06-22","cited_by_count":2,"area":null},{"id":"2607.05476","title":"Parameter-Free Encoders Remain Viable for RDB Foundation Models","created":"2026-07-06","cited_by_count":2,"area":null},{"id":"2404.06709","title":"CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent Layers","created":"2024-04-10","cited_by_count":1,"area":null},{"id":"2501.10187","title":"Good things come in small packages: Should we build AI clusters with Lite-GPUs?","created":"2025-01-17","cited_by_count":1,"area":null},{"id":"2503.20507","title":"Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning","created":"2025-03-26","cited_by_count":1,"area":null},{"id":"2505.17701","title":"COUNTDOWN: Contextually Sparse Activation Filtering Out Unnecessary Weights in Down Projection","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2508.14302","title":"GLASS: Global-Local Aggregation for Inference-time Sparsification of LLMs","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2509.22166","title":"Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2511.04477","title":"Enabling Dynamic Sparsity in Quantized LLM Inference","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2511.22781","title":"The Hidden AI Race: Tracking Environmental Costs of Innovation","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2601.11676","title":"HALO: Semantic-Aware Distributed LLM Inference in Lossy Edge Network","created":"2026-01-16","cited_by_count":1,"area":null},{"id":"2602.15379","title":"FlashMem: Supporting Modern DNN Workloads on Mobile with GPU Memory Hierarchy Optimizations","created":"2026-02-17","cited_by_count":1,"area":null},{"id":"2603.12272","title":"ActTail: Global Activation Sparsity in Large Language Models","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2605.17659","title":"Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2504.03664","title":"PIPO: Pipelined Offloading for Efficient Inference on Consumer Devices","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2512.12744","title":"Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2602.06822","title":"POP: Online Structural Pruning Enables Efficient Inference of Large Foundation Models","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2603.14110","title":"SVD Contextual Sparsity Predictors for Fast LLM Inference","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2604.21026","title":"MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.03109","title":"Gated Subspace Inference for Transformer Acceleration","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.05819","title":"HCInfer: An Efficient Inference System via Error Compensation for Resource-Constrained Devices","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.11678","title":"OOM-Free Alpamayo via CPU-GPU Memory Swapping for Vision-Language-Action Models","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.28961","title":"Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.11357","title":"TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.21712","title":"BatchGen: An Architecture for Scalable and Efficient Batch Inference","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2606.26587","title":"SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2607.10183","title":"Automated Tensor Scheduling for Hybrid CPU-GPU LLM Inference on Consumer Devices","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2607.13735","title":"Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2607.18081","title":"SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2607.22854","title":"Coordinated Networking for On-Device Agent-Augmented Real-Time Communication","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2608.22643","title":"NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching","created":"2026-08-23","cited_by_count":0,"area":null},{"id":"2609.03079","title":"LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.31975","title":"Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.32114","title":"Empowering Hybrid Attention Models on NPUs","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.00465","title":"AIR-LLM: Broadcasting AI Weights over Radio for Memory-Free Edge LLM Inference via RF Computing","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.02598","title":"Activation Sparsity with Weight Approximation for Faster LLM Decoding on Offloaded Weights","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.14745","title":"SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.17321","title":"GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}