yusuke1997/dblp-id-title huggingface dataset:
AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Keyformer: KV Cache reduction through key tokens selection for Efficient Generative Inference
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
ULPPACK: Fast Sub-8-bit Matrix Multiply on Commodity SIMD Hardware
Learning on Distributed Traces for Data Center Storage Systems
Priority-based Parameter Propagation for Distributed DNN Training
Revelio: ML-Generated Debugging Queries for Finding Root Causes in Distributed Systems
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
TorchSparse: Efficient Point Cloud Inference Engine
ProtoRAIL: A Risk-cognizant Imitation Agent for Adaptive vCPU Oversubscription In the Cloud
Cupcake: A Compression Scheduler for Scalable Communication-Efficient Distributed Training
Exploring the Limits of Concurrency in ML Training on Google TPUS
Subgraph Stationary Hardware-Software Inference Co-Design
Riptide: Fast End-to-End Binarized Neural Networks
Radius: Range-based Gradient Sparsity for Large Foundation Model Pre-training
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
Exploiting Hardware Utilization and Adaptive Dataflow for Efficient Sparse Convolution in 3D Point Clouds
Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems
Proceedings of the Seventh Annual Conference on Machine Learning and Systems, MLSys 2024, Santa Clara, CA, USA, May 13-16, 2024
What is the State of Neural Network Pruning?
SLoRA: Scalable Serving of Thousands of LoRA Adapters
Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
JIT-Q: Just-in-time Quantization with Processing-In-Memory for Efficient ML Training
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
Proceedings of the Fifth Conference on Machine Learning and Systems, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
FastTree: Optimizing Attention Kernel and Runtime for Tree-Structured LLM Inference
Proceedings of the Fourth Conference on Machine Learning and Systems, MLSys 2021, virtual, April 5-9, 2021
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Virtual Machine Allocation with Lifetime Predictions
Efficient On-Device Machine Learning with a Biologically-Plausible Forward-Only Algorithm
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator
HeteGen: Efficient Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance
HALOS: Hashing Large Output Space for Cheap Inference
A Learned Performance Model for Tensor Processing Units
Kernel Machines That Adapt To Gpus For Effective Large Batch Training
Gyro Dropout: Maximizing Ensemble Effect in Neural Network Training
AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation
Scaling Deep Learning Training with MPMD Pipeline Parallelism
FlexAttention: A Programming Model for Generating Fused Attention Variants
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
ML-EXray: Visibility into ML Deployment on the Edge
Automatically batching control-intensive programs for modern accelerators
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
TensorFlow.js: Machine Learning For The Web and Beyond
MiLo: Efficient Quantized MoE Inference with Mixture of Low-Rank Compensators
TyXe: Pyro-based Bayesian neural nets for Pytorch
μ-TWO: 3× Faster Multi-Model Training with Orchestration and Memory Optimization
Cortex: A Compiler for Recursive Deep Learning Models
ScaleFusion: Scalable Inference of Spatial-Temporal Diffusion Transformers for High-Resolution Long Video Generation
RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network
NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference
Optimizing DNN Computation with Relaxed Graph Substitutions
Balancing Pipeline Parallelism with Vocabulary Parallelism
Validating Large Language Models with ReLM
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
Efficiently Scaling Transformer Inference
Safe Optimized Static Memory Allocation for Parallel Deep Learning
Breadth-First Pipeline Parallelism
HyC-LoRA: Memory Efficient LoRA Fine-tuning with Hybrid Activation Compression
Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
Sparsity-Aware Memory Interface Architecture using Stacked XORNet Compression for Accelerating Pruned-DNN Models
VIDUR: A Large-Scale Simulation Framework for LLM Inference
ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments
ModularNAS: Towards Modularized and Reusable Neural Architecture Search
SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations
Privacy-Preserving Bandits
Attention-based Learning for Missing Data Imputation in HoloClean
XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
DietCode: Automatic Optimization for Dynamic Tensor Programs
Fluid: Resource-aware Hyperparameter Tuning Engine
A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
Full Deep Neural Network Training On A Pruned Weight Budget
QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
AccMPEG: Optimizing Video Encoding for Accurate Video Analytics
FLStore: Efficient Federated Learning Storage for non-training workloads
Larq Compute Engine: Design, Benchmark and Deploy State-of-the-Art Binarized Neural Networks
To Bridge Neural Network Design and Real-World Performance: A Behaviour Study for Neural Networks
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling
Interference-aware Edge Runtime Prediction with Conformal Matrix Completion
L-GreCo: Layerwise-adaptive Gradient Compression For Efficient Data-parallel Deep Learning
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Proceedings of the Eighth Conference on Machine Learning and Systems, MLSys 2025, Santa Clara, CA, USA, May 12-15, 2025
AutoScratch: ML-Optimized Cache Management for Inference-Oriented GPUs
Scaling Polyhedral Neural Network Verification on GPUs
FirePlace: Placing Firecraker Virtual Machines with Hindsight Imitation
AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation
Proceedings of the Sixth Conference on Machine Learning and Systems, MLSys 2023, Miami, FL, USA, June 4-8, 2023
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Venn: Resource Management For Collaborative Learning Jobs
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods
Distributed Matrix-Based Sampling for Graph Neural Network Training
Accelerate Inference of CNNs for Video Analysis While Preserving Exactness Exploiting Activation Sparsity
Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Youmu: Efficient Columnar Data Pipeline for LLM Training
Parmac: Distributed Optimisation Of Nested Functions, With Application To Learning Binary Autoencoders
SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers
IOS: Inter-Operator Scheduler for CNN Acceleration
FLAML: A Fast and Lightweight AutoML Library
Beyond Data and Model Parallelism for Deep Neural Networks
Transcending Runtime-Memory Tradeoffs in Checkpointing by being Fusion Aware
LeanAttention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers
AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
Seesaw: High-throughput LLM Inference via Model Re-sharding
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
A Deep Learning Based Cost Model for Automatic Code Optimization
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
ApproxCaliper: A Programmable Framework for Application-aware Neural Network Optimization
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems
Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers
Marconi: Prefix Caching for the Era of Hybrid LLMs
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
QMoE: Sub-1-Bit Compression of Trillion Parameter Models
Learning Compressed Embeddings for On-Device Inference
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse
Collapsible Linear Blocks for Super-Efficient Super Resolution
Don't Forget to Sign the Gradients!
In-network Aggregation for Shared Machine Learning Clusters
MLPerf Mobile Inference Benchmark: An Industry-Standard Open-Source Machine Learning Benchmark for On-Device AI
Scaling Video Analytics on Constrained Edge Nodes
Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework
ByzShield: An Efficient and Robust System for Distributed Training
BlueConnect: Decomposing All-Reduce for Deep Learning on Heterogeneous Network Hierarchy
Resource Elasticity in Distributed Deep Learning
TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives
PipeMare: Asynchronous Pipeline Parallel DNN Training
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Proceedings of the Third Conference on Machine Learning and Systems, MLSys 2020, Austin, TX, USA, March 2-4, 2020
Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training
Renee: End-To-End Training of Extreme Classification Models
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
Context Parallelism for Scalable Million-Token Inference
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Tutel: Adaptive Mixture-of-Experts at Scale
Efficient Strong Scaling Through Burst Parallel Training
torch.fx: Practical Program Capture and Transformation for Deep Learning in Python
Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
Spa: Scaling Graph Neural Network Training on Large graphs via Probabilistic splitting
Blink: Fast and Generic Collectives for Distributed ML
Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick
DiffServe: Efficiently Serving Text-to-Image Diffusion Models with Query-Aware Model Scaling
APOLLO: SGD-like Memory, AdamW-level Performance
Atom: Low-Bit Quantization for Efficient and Accurate LLM Serving
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
Federated Optimization in Heterogeneous Networks
Searching for Winograd-aware Quantized Networks
Characterizing and Taming Model Instability Across Edge Devices
On Noisy Evaluation in Federated Hyperparameter Tuning
Randomness in Neural Network Training: Characterizing the Impact of Tooling
TurboAttention: Efficient attention approximation for high throughputs llm
TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning
FLASH: Fast Model Adaptation in ML-Centric Cloud Platforms
Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, Stanford, CA, USA, March 31 - April 2, 2019
Cuttlefish: Low-Rank Model Training without All the Tuning
A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers
Accurate Low-Degree Polynomial Approximation of Non-Polynomial Operators for Fast Private Inference in Homomorphic Encryption
CODE: Compiler-based Neuron-aware Ensemble training
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks
AutoGraph: Imperative-style Coding with Graph-based Performance
Uniform Sparsity in Deep Neural Networks
Pipelined Backpropagation at Scale: Training Large Models without Batches
Hotline Profiler: Automatic Annotation and A Multi-Scale Timeline for Visualizing Time-Use in DNN Training
SOLA: Optimizing SLO Attainment for Large Language Model Serving with State-Aware Scheduling
Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems
Schrodinger's FP Training Neural Networks with Dynamic Floating-Point Containers
ACROBAT: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
FlashDecoding++: Faster Large Language Model Inference with Asynchronization, Flat GEMM Optimization, and Heuristics
PoET-BiN: Power Efficient Tiny Binary Neurons
Be Careful with PyPI Packages: You May Unconsciously Spread Backdoor Model Weights
FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning
Self-Data Distillation for Recovering Quality in Pruned Large Language Models
FlexInfer: Flexible LLM Inference with CPU Computations
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Model Assertions for Monitoring and Improving ML Models
SwiftVI: Time-Efficient Planning and Learning with MDPs
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
COMET: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
VoLUT: Efficient Volumetric streaming enhanced by LUT-based super-resolution
Fine-Tuning Language Models Using Formal Methods Feedback: A Use Case in Autonomous Systems
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
On Optimizing the Communication of Model Parallelism
Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems
Apollo: Automatic Partition-based Operator Fusion through Layer by Layer Optimization
Adaptive Gradient Communication via Critical Learning Regime Identification
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
VQPy: An Object-Oriented Approach to Modern Video Analytics
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems
Restructuring Batch Normalization to Accelerate CNN Training
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
A Distributed Graph-Theoretic Framework for Automatic Parallelization in Multi-core Systems
Reducing Activation Recomputation in Large Transformer Models
UniDM: A Unified Framework for Data Manipulation with Large Language Models
Fine-Grained GPU Sharing Primitives for Deep Learning Applications
Practical Edge Kernels for Integer-Only Vision Transformers Under Post-training Quantization
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
To Compress Or Not To Compress: Understanding The Interactions Between Adversarial Attacks And Neural Network Compression
Efficient Post-training Quantization with FP8 Formats
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
Photon: Federated LLM Pre-Training
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning
FedTree: A Federated Learning System For Trees
MEADOW: Memory-efficient Dataflow and Data Packing for Low Power Edge LLMs
Unified Convolution Framework: A compiler-based approach to support sparse convolutions
The Hidden Bloat in Machine Learning Systems
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
A System for Massively Parallel Hyperparameter Tuning
On Latency Predictors for Neural Architecture Search
PyTorch RPC: Distributed Deep Learning Built on Tensor-Optimized Remote Procedure Calls
FLINT: A Platform for Federated Learning Integration
Understanding the Downstream Instability of Word Embeddings
vMCU: Coordinated Memory Management and Kernel Optimization for DNN Inference on MCUs
MotherNets: Rapid Deep Ensemble Learning
Mas-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-constrained Edge Devices
Does Compressing Activations Help Model Parallel Training?
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
Prompt Cache: Modular Attention Reuse for Low-Latency Inference
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
Efficient GPU Kernels for N: M-Sparse Weights in Deep Learning
YellowFin and the Art of Momentum Tuning
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
Wavelet: Efficient DNN Training with Tick-Tock Scheduling
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs
SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
PAPAYA: Practical, Private, and Scalable Federated Learning
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator
Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation
Improving Model Training with Multi-fidelity Hyperparameter Evaluation
dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large Scale Recommendation
Towards the Co-design of Neural Networks and Accelerators
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
Punica: Multi-Tenant LoRA Serving
On the Utility of Gradient Compression in Distributed Training Systems
Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training
Pytorch-BigGraph: A Large Scale Graph Embedding System
Value Learning for Throughput Optimization of Deep Learning Workloads
Scaling Distributed Training with Adaptive Summation
Pufferfish: Communication-efficient Models At No Extra Cost
Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer
FedTrans: Efficient Federated Learning via Multi-Model Transformation
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Predictive Precompute with Recurrent Neural Networks
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
FROTE: Feedback Rule-Driven Oversampling for Editing Models
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs
SIRIUS: Harvesting Whole-Program Optimization Opportunities for DNNs
COMET: Neural Cost Model Explanation Framework
MNN: A Universal and Efficient Inference Engine
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
Optimizing LLM Queries in Relational Data Analytics Workloads
Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache
LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD
SLA-Driven ML Inference Framework for Clouds with Hetergeneous Accelerators
PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices
Proteus: Preserving Model Confidentiality during Graph Optimizations
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors
GPU Semiring Primitives for Sparse Neighborhood Methods
Supply-Chain Attacks in Machine Learning Frameworks
REX: Revisiting Budgeted Training with an Improved Schedule
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval
RL-Scope: Cross-stack Profiling for Deep Reinforcement Learning Workloads
Bit Error Robustness for Energy-Efficient DNN Accelerators
Work-in-Progress: Models and Tools to Detect Real-Time Scheduling Anomalies
Segment Streaming for the Three-Phase Execution Model: Design and Implementation
MAX: Advanced General Purpose Real-Time Multicomputer for Space Applications
Modeling Out-of-Order Processors for Software Timing Analysis
Linux's Processor Affinity API, Refined: Shifting Real-Time Tasks Towards Higher Schedulability
Transient Overloads in Fault-Tolerant Real-Time Systems
Symbolic Robustness Analysis
Scheduling real-time applications in an open environment
16th IEEE Real-Time Systems Symposium, Palazzo dei Congressi, Via Matteotti, 1, Pisa, Italy, December 4-7, 1995, Proceedings
Scheduling and communication in MetaH
Hardware Support for Priority Inheritance
Outline of a Real Time Data Flow Language
Work-in-Progress: Lessons learnt from creating an Extreme Value Library in Python
Battery- and Aging-Aware Embedded Control Systems for Electric Vehicles