{"total":47,"rows":[{"id":"2205.14135","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","created":"2022-05-27","cited_by_count":3673,"area":null},{"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":"2203.17149","title":"AEGNN: Asynchronous Event-based Graph Neural Networks","created":"2022-03-31","cited_by_count":118,"area":null},{"id":"2310.04292","title":"Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets","created":"2023-10-06","cited_by_count":39,"area":null},{"id":"2103.06995","title":"Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking","created":"2021-03-11","cited_by_count":30,"area":null},{"id":"2206.06178","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time","created":"2022-06-13","cited_by_count":26,"area":null},{"id":"2201.12465","title":"Flashlight: Enabling Innovation in Tools for Machine Learning","created":"2022-01-29","cited_by_count":25,"area":null},{"id":"2306.15552","title":"A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms","created":"2023-06-27","cited_by_count":21,"area":null},{"id":"2309.17224","title":"Training and inference of large language models using 8-bit floating point","created":"2023-09-29","cited_by_count":20,"area":null},{"id":"2305.19454","title":"Dynamic Sparsity Is Channel-Level Sparsity Learner","created":"2023-05-30","cited_by_count":18,"area":null},{"id":"2303.11257","title":"Unit Scaling: Out-of-the-Box Low-Precision Training","created":"2023-03-20","cited_by_count":17,"area":null},{"id":"2203.12852","title":"Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2105.10430","title":"Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units","created":"2021-05-21","cited_by_count":10,"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":"2302.10872","title":"MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation","created":"2023-02-21","cited_by_count":9,"area":null},{"id":"2310.04467","title":"Design Principles for Lifelong Learning AI Accelerators","created":"2023-10-05","cited_by_count":9,"area":null},{"id":"2107.02027","title":"Efficient Sequence Packing without Cross-contamination: Accelerating Large Language Models without Impacting Performance","created":"2021-06-29","cited_by_count":8,"area":null},{"id":"2304.05511","title":"Training Large Language Models Efficiently with Sparsity and Dataflow","created":"2023-04-11","cited_by_count":7,"area":null},{"id":"2207.05278","title":"Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensors","created":"2022-07-12","cited_by_count":5,"area":null},{"id":"2311.04417","title":"Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs","created":"2023-11-08","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":"2205.06287","title":"Adaptive Block Floating-Point for Analog Deep Learning Hardware","created":"2022-05-12","cited_by_count":4,"area":null},{"id":"2311.04386","title":"Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models","created":"2023-11-07","cited_by_count":4,"area":null},{"id":"2211.13853","title":"Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry","created":"2022-11-25","cited_by_count":3,"area":null},{"id":"2303.05641","title":"Efficient Real Time Recurrent Learning through combined activity and parameter sparsity","created":"2023-03-10","cited_by_count":3,"area":null},{"id":"2412.11810","title":"Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory","created":"2024-12-16","cited_by_count":3,"area":null},{"id":"2106.05822","title":"GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures","created":"2021-06-10","cited_by_count":2,"area":null},{"id":"2203.16255","title":"Physics Community Needs, Tools, and Resources for Machine Learning","created":"2022-03-30","cited_by_count":2,"area":null},{"id":"2409.12994","title":"Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML","created":"2024-09-19","cited_by_count":2,"area":null},{"id":"2104.02188","title":"GPU Domain Specialization via Composable On-Package Architecture","created":"2021-04-05","cited_by_count":1,"area":null},{"id":"2210.12247","title":"Benchmarking GPU and TPU Performance with Graph Neural Networks","created":"2022-10-21","cited_by_count":1,"area":null},{"id":"2301.13637","title":"Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis","created":"2023-01-31","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":"2311.03216","title":"Mini Minds: Exploring Bebeshka and Zlata Baby Models","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2508.18850","title":"ClusterFusion: Expanding Operator Fusion Scope for LLM Inference via Cluster-Level Collective Primitive","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2601.04382","title":"Radiant Foam Rendering on a Graph Processor","created":"2026-01-07","cited_by_count":1,"area":null},{"id":"2106.12810","title":"A Construction Kit for Efficient Low Power Neural Network Accelerator Designs","created":"2021-06-24","cited_by_count":0,"area":null},{"id":"2109.12191","title":"NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU","created":"2021-09-24","cited_by_count":0,"area":null},{"id":"2309.08946","title":"Reducing Memory Requirements for the IPU using Butterfly Factorizations","created":"2023-09-16","cited_by_count":0,"area":null},{"id":"2311.01135","title":"Generating QM1B with PySCF$_{\\text{IPU}}$","created":"2023-11-02","cited_by_count":0,"area":null},{"id":"2312.03120","title":"The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning","created":"2023-12-05","cited_by_count":0,"area":null},{"id":"2403.07786","title":"Physics-informed generative real-time lens-free imaging","created":"2024-03-12","cited_by_count":0,"area":null},{"id":"2406.09726","title":"PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief Propagation","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2503.06342","title":"Exploring the Performance Improvement of Tensor Processing Engines through Transformation in the Bit-weight Dimension of MACs","created":"2025-03-08","cited_by_count":0,"area":null},{"id":"2507.11506","title":"ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2601.19904","title":"DABench-LLM: Standardized and In-Depth Benchmarking of Post-Moore Dataflow AI Accelerators for LLMs","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2604.12891","title":"TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning","created":"2026-04-14","cited_by_count":0,"area":null}],"next_cursor":null}