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ApproxHPVM: a portable compiler IR for accuracy-aware optimizations
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-23702-7_24openalex-snapshot
Probabilistically Accurate Program Transformations
inferredcites →https://doi.org/10.1109/cvpr.2004.383openalex-snapshot
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/hotchips.2013.7478287openalex-snapshot
HSAIL: Portable compiler IR for HSA
inferredcites →https://doi.org/10.1109/hpec.2017.8091072openalex-snapshot
Exploiting half precision arithmetic in Nvidia GPUs
inferredcites →https://doi.org/10.1109/icme.2007.4284808openalex-snapshot
Robust Transmission of JPEG2000 Encoded Images Over Packet Loss Channels
inferredcites →https://doi.org/10.1109/ipdps.2009.5160991openalex-snapshot
Best-effort parallel execution framework for Recognition and mining applications
inferredcites →https://doi.org/10.1109/ipdps.2010.5470469openalex-snapshot
Exploiting the forgiving nature of applications for scalable parallel execution
inferredcites →https://doi.org/10.1109/isca.2014.6853213openalex-snapshot
General-purpose code acceleration with limited-precision analog computation
inferredcites →https://doi.org/10.1109/isca.2016.40openalex-snapshot
Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/isca.2016.42openalex-snapshot
Cambricon: An Instruction Set Architecture for Neural Networks
inferredcites →https://doi.org/10.1109/isca.2018.00015openalex-snapshot
PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms
inferredcites →https://doi.org/10.1109/jssc.2018.2867275openalex-snapshot
A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training
inferredcites →https://doi.org/10.1109/micro.2012.48openalex-snapshot
Neural Acceleration for General-Purpose Approximate Programs
inferredcites →https://doi.org/10.1109/micro.2014.58openalex-snapshot
DaDianNao: A Machine-Learning Supercomputer
inferredcites →https://doi.org/10.1109/tip.2005.860338openalex-snapshot
Optimized transmission of JPEG2000 streams over wireless channels
inferredcites →https://doi.org/10.1145/1183401.1183447openalex-snapshot
Probabilistic accuracy bounds for fault-tolerant computations that discard tasks
inferredcites →https://doi.org/10.1145/1950365.1950390openalex-snapshot
Dynamic knobs for responsive power-aware computing
inferredcites →https://doi.org/10.1145/2025113.2025133openalex-snapshot
Managing performance vs. accuracy trade-offs with loop perforation
inferredcites →https://doi.org/10.1145/2103621.2103710openalex-snapshot
Randomized accuracy-aware program transformations for efficient approximate computations
inferredcites →https://doi.org/10.1145/2103656.2103710openalex-snapshot
Randomized accuracy-aware program transformations for efficient approximate computations
inferredcites →https://doi.org/10.1145/2465787.2465790openalex-snapshot
Parallelizing Sequential Programs with Statistical Accuracy Tests
inferredcites →https://doi.org/10.1145/2509136.2509546openalex-snapshot
Verifying quantitative reliability for programs that execute on unreliable hardware
inferredcites →https://doi.org/10.1145/2594291.2594302openalex-snapshot
Stochastic optimization of floating-point programs with tunable precision
inferredcites →https://doi.org/10.1145/2678373.2665746openalex-snapshot
General-purpose code acceleration with limited-precision analog computation
inferredcites →https://doi.org/10.1145/2737924.2737969openalex-snapshot
Autotuning algorithmic choice for input sensitivity
inferredcites →https://doi.org/10.1145/2814270.2814301openalex-snapshot
Probability type inference for flexible approximate programming
inferredcites →https://doi.org/10.1145/2858965.2814301openalex-snapshot
Probability type inference for flexible approximate programming
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1410.0759openalex-snapshot
cuDNN: Efficient Primitives for Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1511.06393openalex-snapshot
Fixed Point Quantization of Deep Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1802.04799openalex-snapshot
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1805.00907openalex-snapshot
Glow: Graph Lowering Compiler Techniques for Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1809.05859openalex-snapshot
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms
inferredcites →https://doi.org/10.5555/2190025.2190056openalex-snapshot
Language and compiler support for auto-tuning variable-accuracy algorithms
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-93848-1_13openalex-snapshot
A Proposal for an Adaptive Compiler Architecture and Design for Precision and Scale Aware Compilation Using Data-Oriented Design
inferred← citeshttps://doi.org/10.1109/cases51649.2020.9243814openalex-snapshot
Towards Quality-Driven Approximate Software Generation for Accurate Hardware: Work-in-Progress
inferred← citeshttps://doi.org/10.1109/jiot.2024.3365957openalex-snapshot
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles
inferred← citeshttps://doi.org/10.1109/metacom57706.2023.00042openalex-snapshot
Challenges in Metaverse Research: An Internet of Things Perspective
inferred← citeshttps://doi.org/10.1109/mitp.2023.3254642openalex-snapshot
Approximate High-Performance Computing: A Fast and Energy-Efficient Computing Paradigm in the Post-Moore Era
inferred← citeshttps://doi.org/10.1109/mm.2022.3186547openalex-snapshot
HPVM: Hardware-Agnostic Programming for Heterogeneous Parallel Systems
inferred← citeshttps://doi.org/10.1109/sc41406.2024.00078openalex-snapshot
HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
inferred← citeshttps://doi.org/10.1109/smart66937.2025.11389313openalex-snapshot
AI-Augmented Compiler Optimization for Energyefficient Software Execution on Embedded Systems
inferred← citeshttps://doi.org/10.1109/tc.2020.3005083openalex-snapshot
XMeter: Finding Approximable Functions and Predicting Their Accuracy
inferred← citeshttps://doi.org/10.1145/3453483.3454042openalex-snapshot
Fluid: a framework for approximate concurrency via controlled dependency relaxation
inferred← citeshttps://doi.org/10.1145/3458817.3476177openalex-snapshot
Accelerating applications using edge tensor processing units
inferred← citeshttps://doi.org/10.1145/3524053.3542746openalex-snapshot
Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery Tasks
inferred← citeshttps://doi.org/10.1145/3581784.3607095openalex-snapshot
HPAC-Offload: Accelerating HPC Applications with Portable Approximate Computing on the GPU
inferred← citeshttps://doi.org/10.1145/3620666.3651351openalex-snapshot
Fractal: Joint Multi-Level Sparse Pattern Tuning of Accuracy and Performance for DNN Pruning
inferred← citeshttps://doi.org/10.1145/3784828.3785420openalex-snapshot
Adaptive Approximation-Aware Scheduling for Heterogeneous Computing Using Reinforcement Learning
inferred← citeshttps://doi.org/10.3390/microelectronics2020010openalex-snapshot
Bridging Architectures, Mapping, and Learning for DNN Acceleration with Processing-in-Memory and In-Memory Computing Systems
inferred← citeshttps://doi.org/10.48550/arxiv.2107.05473openalex-snapshot
GPTPU: Accelerating Applications using Edge Tensor Processing Units
inferred← citeshttps://doi.org/10.70675/ff8907ebzb70cz4fbdzad39z2513988c524copenalex-snapshot
Self-adaptable operational semantics
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