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Survey-of-Machine-Learning-Systems

Table of Contents

  • An Optimizing Framework on MLIR for Efficient FPGA-based Accelerator Generation Link
  • E2EMap: End-to-End Reinforcement Learning for CGRA Compilation via Reverse Mapping Link
  • PruneGNN: Algorithm-Architecture Pruning Framework for Graph Neural Network Acceleration Link
  • RELIEF: Relieving Memory Pressure In SoCs Via Data Movement-Aware Accelerator Scheduling Link
  • ZENO: A Type-based Optimization Framework for Zero Knowledge Neural Network Inference Link
  • Carat: Unlocking Value-Level Parallelism for Multiplier-Free GEMMs Link
  • FPGA Technology Mapping Using Sketch-Guided Program Synthesis Link
  • MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training Link
  • PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization Link
  • TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks Link
  • 8-bit Transformer Inference and Fine-tuning for Edge Accelerators Link
  • EVT: Accelerating Deep Learning Training with Epilogue Visitor Tree Link
  • GMorph: Accelerating Multi-DNN Inference via Model Fusion Link
  • Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance Link
  • Minuet: Accelerating 3D Sparse Convolutions on GPUs Link
  • Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications Link