On the public citation graph
SimNet
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/dasc.2014.85openalex-snapshot
Cache Simulation for Instruction Set Simulator QEMU
inferredcites →https://doi.org/10.1109/hpca.2015.7056063openalex-snapshot
GPGPU performance and power estimation using machine learning
inferredcites →https://doi.org/10.1109/iccd.1996.563595openalex-snapshot
Reducing state loss for effective trace sampling of superscalar processors
inferredcites →https://doi.org/10.1109/iiswc.2015.29openalex-snapshot
Full Speed Ahead: Detailed Architectural Simulation at Near-Native Speed
inferredcites →https://doi.org/10.1109/isca.2001.937425openalex-snapshot
Proceedings 28th Annual International Symposium on Computer Architecture
inferredcites →https://doi.org/10.1109/isca.2018.00014openalex-snapshot
FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud
inferredcites →https://doi.org/10.1109/ispass.2014.6844456openalex-snapshot
BarrierPoint: Sampled simulation of multi-threaded applications
inferredcites →https://doi.org/10.1109/ispass.2014.6844457openalex-snapshot
Sources of error in full-system simulation
inferredcites →https://doi.org/10.1109/micro.2004.28openalex-snapshot
Pinpointing Representative Portions of Large Intel® Itanium® Programs with Dynamic Instrumentation
inferredcites →https://doi.org/10.1109/micro50266.2020.00046openalex-snapshot
Predicting Execution Times With Partial Simulations in Virtual Memory Research: Why and How
inferredcites →https://doi.org/10.1109/mm.2006.79openalex-snapshot
SimFlex: Statistical Sampling of Computer System Simulation
inferredcites →https://doi.org/10.1109/samos.2015.7363659openalex-snapshot
Learning-based analytical cross-platform performance prediction
inferredcites →https://doi.org/10.1109/sbac-pad.2014.30openalex-snapshot
Predicting GPU Performance from CPU Runs Using Machine Learning
inferredcites →https://doi.org/10.1109/sbac-pad.2017.23openalex-snapshot
A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs
inferredcites →https://doi.org/10.1145/1168857.1168882openalex-snapshot
Efficiently exploring architectural design spaces via predictive modeling
inferredcites →https://doi.org/10.1145/1229428.1229479openalex-snapshot
Methods of inference and learning for performance modeling of parallel applications
inferredcites →https://doi.org/10.1145/1669112openalex-snapshot
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
inferredcites →https://doi.org/10.1145/2145816openalex-snapshot
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
inferredcites →https://doi.org/10.1145/2830772.2830780openalex-snapshot
Cross-architecture performance prediction (XAPP) using CPU code to predict GPU performance
inferredcites →https://doi.org/10.1145/2897937.2897977openalex-snapshot
Accurate phase-level cross-platform power and performance estimation
inferredcites →https://doi.org/10.1145/3079856.3080246openalex-snapshot
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3126557openalex-snapshot
GPU Performance Estimation using Software Rasterization and Machine Learning
inferredcites →https://doi.org/10.1145/605397.605403openalex-snapshot
Automatically characterizing large scale program behavior
inferredcites →https://doi.org/10.1145/885651.781076openalex-snapshot
Using SimPoint for accurate and efficient simulation
inferredcites →https://doi.org/10.4018/jdst.2010040104openalex-snapshot
A Simulator for Large-Scale Parallel Computer Architectures
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1109/icsft66733.2026.11507605openalex-snapshot
Python-Based Simulator for Machine Learning-Driven Adaptive Hazard Mitigation in 5-Stage RISC Pipelines
inferred← citeshttps://doi.org/10.1109/iiswc66894.2025.00025openalex-snapshot
Learning Architectural Cache Simulator Behaviour
inferred← citeshttps://doi.org/10.1109/sc41404.2022.00084openalex-snapshot
Scalable Deep Learning-Based Microarchitecture Simulation on GPUs
inferred← citeshttps://doi.org/10.1109/sc41406.2024.00072openalex-snapshot
Learning Generalizable Program and Architecture Representations for Performance Modeling
inferred← citeshttps://doi.org/10.1109/tpds.2026.3706975openalex-snapshot
HARP: Heterogeneous Analog-Digital Resource-Aware Performance and Scheduling Framework for Transformer Acceleration
inferred← citeshttps://doi.org/10.1145/3652963.3655085openalex-snapshot
TAO: Re-Thinking DL-based Microarchitecture Simulation
inferred← citeshttps://doi.org/10.1145/3656012openalex-snapshot
TAO: Re-Thinking DL-based Microarchitecture Simulation
inferred← citeshttps://doi.org/10.1145/3702986openalex-snapshot
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
inferred← citeshttps://doi.org/10.23919/date69613.2026.11539194openalex-snapshot
Machine Learning-Driven Early Performance Prediction Framework for Accelerated Microarchitecture Simulation
inferred← citeshttps://doi.org/10.4018/978-1-6684-3795-7.ch017openalex-snapshot
Architectures of High-Performance Computing Systems for Machine Learning Workloads
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