{"total":29,"rows":[{"id":"2103.05579","title":"hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices","created":"2021-03-09","cited_by_count":62,"area":null},{"id":"2101.00443","title":"Semantics for Robotic Mapping, Perception and Interaction: A Survey","created":"2021-01-02","cited_by_count":56,"area":null},{"id":"2101.05108","title":"Fast convolutional neural networks on FPGAs with hls4ml","created":"2021-01-13","cited_by_count":42,"area":null},{"id":"2309.11932","title":"A Machine Learning-oriented Survey on Tiny Machine Learning","created":"2023-09-21","cited_by_count":27,"area":null},{"id":"2103.16701","title":"Charged particle tracking via edge-classifying interaction networks","created":"2021-03-30","cited_by_count":19,"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":"2201.01863","title":"CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs","created":"2022-01-05","cited_by_count":11,"area":null},{"id":"2101.04989","title":"Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features","created":"2021-01-13","cited_by_count":10,"area":null},{"id":"2206.11791","title":"Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark","created":"2022-06-23","cited_by_count":9,"area":null},{"id":"2107.04197","title":"REX: Revisiting Budgeted Training with an Improved Schedule","created":"2021-07-09","cited_by_count":8,"area":null},{"id":"2407.08356","title":"Event-based vision on FPGAs -- a survey","created":"2024-07-11","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":"2108.13732","title":"Deep Learning on Edge TPUs","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2203.12091","title":"FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation","created":"2022-03-22","cited_by_count":2,"area":null},{"id":"2311.05726","title":"Neural Network Methods for Radiation Detectors and Imaging","created":"2023-11-09","cited_by_count":2,"area":null},{"id":"2506.03938","title":"FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review","created":"2025-06-04","cited_by_count":2,"area":null},{"id":"2104.04576","title":"Compiler Toolchains for Deep Learning Workloads on Embedded Platforms","created":"2021-03-08","cited_by_count":1,"area":null},{"id":"2309.01945","title":"On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2310.15274","title":"From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges","created":"2023-10-23","cited_by_count":1,"area":null},{"id":"2311.14005","title":"When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence","created":"2023-11-23","cited_by_count":1,"area":null},{"id":"2101.10534","title":"Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis","created":"2021-01-24","cited_by_count":0,"area":null},{"id":"2102.00628","title":"Spatiotemporal Ground Reaction Force Analysis using Convolutional Neural Networks to Analyze Parkinsonian Gait","created":"2021-02-01","cited_by_count":0,"area":null},{"id":"2106.10652","title":"TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC","created":"2021-06-20","cited_by_count":0,"area":null},{"id":"2110.04861","title":"A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization","created":"2021-10-10","cited_by_count":0,"area":null},{"id":"2202.09461","title":"Towards Enabling Dynamic Convolution Neural Network Inference for Edge Intelligence","created":"2022-02-18","cited_by_count":0,"area":null},{"id":"2209.11272","title":"Optimization of FPGA-based CNN Accelerators Using Metaheuristics","created":"2022-09-22","cited_by_count":0,"area":null},{"id":"2309.12202","title":"Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023","created":"2023-09-14","cited_by_count":0,"area":null},{"id":"2405.14556","title":"Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels","created":"2024-05-23","cited_by_count":0,"area":null},{"id":"2603.08740","title":"Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review","created":"2026-02-25","cited_by_count":0,"area":null}],"next_cursor":null}