{"total":54,"rows":[{"id":"2207.01405","title":"I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference","created":"2022-07-04","cited_by_count":95,"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":"2305.10435","title":"Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions","created":"2023-05-11","cited_by_count":55,"area":null},{"id":"2206.08898","title":"SimA: Simple Softmax-free Attention for Vision Transformers","created":"2022-06-17","cited_by_count":32,"area":null},{"id":"2112.02191","title":"NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference","created":"2021-12-03","cited_by_count":25,"area":null},{"id":"2303.07470","title":"X-Former: In-Memory Acceleration of Transformers","created":"2023-03-13","cited_by_count":25,"area":null},{"id":"2503.02891","title":"Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies","created":"2025-02-26","cited_by_count":24,"area":null},{"id":"2405.17849","title":"I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models","created":"2024-05-28","cited_by_count":23,"area":null},{"id":"2309.01729","title":"Softmax Bias Correction for Quantized Generative Models","created":"2023-09-04","cited_by_count":15,"area":"diffusion"},{"id":"2412.11702","title":"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads","created":"2024-12-16","cited_by_count":15,"area":null},{"id":"2402.10930","title":"ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters","created":"2024-01-31","cited_by_count":14,"area":null},{"id":"2405.00314","title":"Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey","created":"2024-05-01","cited_by_count":13,"area":null},{"id":"2406.14854","title":"PEANO-ViT: Power-Efficient Approximations of Non-Linearities in Vision Transformers","created":"2024-06-21","cited_by_count":13,"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":"2307.03493","title":"ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers","created":"2023-07-07","cited_by_count":12,"area":null},{"id":"2511.06767","title":"QUARK: Quantization-Enabled Circuit Sharing for Transformer Acceleration by Exploiting Common Patterns in Nonlinear Operations","created":"2025-11-10","cited_by_count":11,"area":null},{"id":"2206.14550","title":"SALO: An Efficient Spatial Accelerator Enabling Hybrid Sparse Attention Mechanisms for Long Sequences","created":"2022-06-29","cited_by_count":10,"area":null},{"id":"2111.10770","title":"Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism","created":"2021-11-21","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":"2204.03227","title":"Accelerating Attention through Gradient-Based Learned Runtime Pruning","created":"2022-04-07","cited_by_count":8,"area":null},{"id":"2407.11041","title":"Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT","created":"2024-07-06","cited_by_count":6,"area":null},{"id":"2505.14314","title":"Low-Cost FlashAttention with Fused Exponential and Multiplication Hardware Operators","created":"2025-05-20","cited_by_count":5,"area":null},{"id":"2510.16040","title":"Kelle: Co-design KV Caching and eDRAM for Efficient LLM Serving in Edge Computing","created":"2025-10-16","cited_by_count":5,"area":"inference"},{"id":"2510.17189","title":"SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference","created":"2025-10-20","cited_by_count":5,"area":null},{"id":"2311.09775","title":"MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization","created":"2023-11-16","cited_by_count":4,"area":null},{"id":"2404.11788","title":"Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads","created":"2024-04-17","cited_by_count":4,"area":null},{"id":"2409.05902","title":"OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models","created":"2024-09-06","cited_by_count":4,"area":null},{"id":"2411.17847","title":"SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on Associative Processors","created":"2024-11-26","cited_by_count":4,"area":null},{"id":"2504.11227","title":"VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers","created":"2025-04-15","cited_by_count":4,"area":null},{"id":"2202.12411","title":"TrimBERT: Tailoring BERT for Trade-offs","created":"2022-02-24","cited_by_count":3,"area":null},{"id":"2402.10118","title":"Reusing Softmax Hardware Unit for GELU Computation in Transformers","created":"2024-02-15","cited_by_count":3,"area":null},{"id":"2410.03185","title":"EXAQ: Exponent Aware Quantization For LLMs Acceleration","created":"2024-10-04","cited_by_count":3,"area":null},{"id":"2411.03697","title":"TATAA: Programmable Mixed-Precision Transformer Acceleration with a Transformable Arithmetic Architecture","created":"2024-11-06","cited_by_count":3,"area":null},{"id":"2511.21513","title":"IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference","created":"2025-11-26","cited_by_count":3,"area":null},{"id":"2403.19591","title":"Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers","created":"2024-03-28","cited_by_count":2,"area":null},{"id":"2405.06038","title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2408.12742","title":"TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing","created":"2024-08-22","cited_by_count":2,"area":null},{"id":"2507.16323","title":"SpeLLM: Character-Level Multi-Head Decoding","created":"2025-07-22","cited_by_count":2,"area":null},{"id":"2507.00797","title":"VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator","created":"2025-07-01","cited_by_count":1,"area":"inference"},{"id":"2602.02071","title":"BAPS: A Fine-Grained Low-Precision Scheme for Softmax in Attention via Block-Aware Precision reScaling","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2602.02988","title":"NLI:Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2402.01169","title":"Faster Inference of Integer SWIN Transformer by Removing the GELU Activation","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2405.04206","title":"NOVA: NoC-based Vector Unit for Mapping Attention Layers on a CNN Accelerator","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2412.00408","title":"QuAKE: Speeding up Model Inference Using Quick and Approximate Kernels for Exponential Non-Linearities","created":"2024-11-30","cited_by_count":0,"area":null},{"id":"2505.02181","title":"Efficient FPGA Implementation of Time-Domain Popcount for Low-Complexity Machine Learning","created":"2025-05-04","cited_by_count":0,"area":null},{"id":"2505.21535","title":"FAR: Function-preserving Attention Replacement for IMC-friendly Inference","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2510.06834","title":"Vectorized FlashAttention with Low-cost Exponential Computation in RISC-V Vector Processors","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2601.10823","title":"Mugi: Value Level Parallelism For Efficient LLMs","created":"2026-01-15","cited_by_count":0,"area":null},{"id":"2604.02292","title":"Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inference","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2604.23647","title":"Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices","created":"2026-04-26","cited_by_count":0,"area":null},{"id":"2606.17781","title":"MIVE: A Minimalist Integer Vector Engine for Softmax LayerNorm and RMSNorm Acceleration","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2609.04266","title":"Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.33586","title":"Approximating Softmax in Pretrained LLMs: Model Sensitivity and Kernel Acceleration","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.33591","title":"Pretraining Transformers with Quantized Softmax in Attention","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}