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FedTree: A Federated Learning System For Trees
Efficiently Scaling Transformer Inference
Transcending Runtime-Memory Tradeoffs in Checkpointing by being Fusion Aware
Be Careful with PyPI Packages: You May Unconsciously Spread Backdoor Model Weights
X-RLFLOW: GRAPH REINFORCEMENT LEARNING FOR NEURAL NETWORK SUBGRAPHS TRANSFORMATION
FLINT: A Platform for Federated Learning Integration
Cupcake: A Compression Scheduler for Scalable Communication-Efficient Distributed Training
Renee: END-TO-END TRAINING OF EXTREME CLASSIFICATION MODELS
SIRIUS: Harvesting Whole-Program Optimization Opportunities for DNNs
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network
Building Verified Neural Networks for Computer Systems with Ouroboros
ApproxCaliper: A Programmable Framework for Application-aware Neural Network Optimization
Reducing Activation Recomputation in Large Transformer Models
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices
Validating Large Language Models with ReLM
Hotline Profiler: Automatic Annotation and A Multi-Scale Timeline for Visualizing Time-Use in DNN Training
Practical Edge Kernels for Integer-Only Vision Transformers Under Post-training Quantization
Breadth-First Pipeline Parallelism
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
On Noisy Evaluation in Federated Hyperparameter Tuning
Safe Optimized Static Memory Allocation for Parallel Deep Learning
PyTorch RPC: Distributed Deep Learning Built on Tensor-Optimized Remote Procedure Calls
Exploiting Hardware Utilization and Adaptive Dataflow for Efficient Sparse Convolution in 3D Point Clouds
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Unified Convolution Framework: A compiler-based approach to support sparse convolutions
Cuttlefish: Low-Rank Model Training without All the Tuning
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Edge Impulse: An MLOps Platform for Tiny Machine Learning
On Optimizing the Communication of Model Parallelism
Tutel: Adaptive Mixture-of-Experts at Scale
Efficient GPU Kernels for N:M-Sparse Weights in Deep Learning
SUBGRAPH STATIONARY HARDWARE-SOFTWARE INFERENCE CO-DESIGN
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
AutoScratch: ML-Optimized Cache Management for Inference-Oriented GPUs
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sparsity-Aware Memory Interface Architecture using Stacked XORNet Compression for Accelerating Pruned-DNN Models
HyperGef: A Framework Enabling Efficient Fusion for Hypergraph Neural Network on GPUs
μ-TWO: 3× Faster Multi-Model Training with Orchestration and Memory Optimization
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Virtual Machine Allocation with Lifetime Predictions
Uniform Sparsity in Deep Neural Networks
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