https://github.com/Azzam-Alhussain/QDCGAN-PYNQ
공식 코드Hardware-Efficient Deconvolution-Based GAN for Edge Computing
Papers with Code 아카이브
High-Level Synthesis
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVlabs/CompareXplore
공식 코드Learning to Compare Hardware Designs for High-Level Synthesis
https://github.com/PingChang818/DID4HLS
공식 코드https://github.com/Richielee630/TMMA
공식 코드Design and Implementation of an FPGA-Based Hardware Accelerator for Transformer
https://github.com/UT-LCA/ML4Accel-Dataset/tree/main/fpga_ml_dataset
공식 코드HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis
https://github.com/Yang-YiFan/DiracDeltaNet
공식 코드Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
https://github.com/Zheyu-Rain/APT-HLS-AI
공식 코드https://github.com/bingleilou/fsead
공식 코드fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library
https://github.com/bjourne/fast_snn_fpga
공식 코드Fast Algorithms for Spiking Neural Network Simulation with FPGAs
https://github.com/chenfengzhao/gnnhls
공식 코드GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
https://github.com/cornell-zhang/allo
공식 코드Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
https://github.com/cornell-zhang/allo-pldi24-artifact
공식 코드https://github.com/danielholanda/LeFlow
공식 코드LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
https://github.com/fastmachinelearning/nac-opt
공식 코드https://github.com/hls-fpga-machine-learning/hls4ml
공식 코드Fast inference of deep neural networks in FPGAs for particle physics
https://github.com/hlstransform/submission
공식 코드HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level Synthesis
https://github.com/impetus-udes/rule4ml
공식 코드rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA
https://github.com/nozidoali/llm-dse
공식 코드https://github.com/sarashs/fpga_agi
공식 코드SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation
https://github.com/sharc-lab/dgnn-booster
공식 코드DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference
https://github.com/sharc-lab/edge-moe
공식 코드https://github.com/sharc-lab/gengnn
공식 코드GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
https://github.com/sharc-lab/hlsfactory
공식 코드HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond
https://github.com/sharc-lab/inr-arch
공식 코드https://github.com/sjtu-zhao-lab/hierarchical-gnn-for-hls
공식 코드Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis with GNNs
https://github.com/stefanpie/hls-eval
공식 코드HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks
https://github.com/ucb-bar/autophase
공식 코드AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
https://github.com/walkieq/RNN_HLS
공식 코드Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
https://github.com/walkieq/gnn-jedinet-fpga
공식 코드LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
https://github.com/weikai-li/hierarchicalmoe
공식 코드Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
https://github.com/yuchaoliao/vaegan
공식 코드https://github.com/zeusss9/kan_analysis
공식 코드https://github.com/zlinaf/powergear
공식 코드PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs
https://github.com/TheKivs/LHC_Jet_Tagging
Fast inference of deep neural networks in FPGAs for particle physics
https://github.com/azzam-alhussain/fpga-qhar
Hardware-Efficient Deconvolution-Based GAN for Edge Computing
https://github.com/cristian856/HelloWolrd
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.