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Posits and the state of numerical representations in the age of exascale and edge computing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-37277-4_25openalex-snapshot
A Fast Approximation of the Hyperbolic Tangent When Using Posit Numbers and Its Application to Deep Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-76526-6openalex-snapshot
Handbook of Floating-Point Arithmetic
inferredcites →https://doi.org/10.1007/978-3-319-76526-6_5openalex-snapshot
Enhanced Floating-Point Sums, Dot Products, and Polynomial Values
inferredcites →https://doi.org/10.1007/978-981-10-8612-0_66openalex-snapshot
A Design Framework for Instrumenting Analytic Provenance for Problem-Solving Tasks
inferredcites →https://doi.org/10.1007/bf02013227openalex-snapshot
Mean wall thickness of trabecular bone packets in the human iliac crest: Changes with age
inferredcites →https://doi.org/10.1016/j.dsp.2020.102762openalex-snapshot
Deep PeNSieve: A deep learning framework based on the posit number system
inferredcites →https://doi.org/10.1016/j.future.2013.01.010openalex-snapshot
Internet of Things (IoT): A vision, architectural elements, and future directions
inferredcites →https://doi.org/10.1029/2020ms002246openalex-snapshot
Number Formats, Error Mitigation, and Scope for 16‐Bit Arithmetics in Weather and Climate Modeling Analyzed With a Shallow Water Model
inferredcites →https://doi.org/10.1109/access.2019.2920936openalex-snapshot
PACoGen: A Hardware Posit Arithmetic Core Generator
inferredcites →https://doi.org/10.1109/arith.2019.00019openalex-snapshot
Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations
inferredcites →https://doi.org/10.1109/arith51176.2021.00017openalex-snapshot
Shadow computation with BFloat16 to estimate the numerical accuracy of summations
inferredcites →https://doi.org/10.1109/bigdata.2018.8622520openalex-snapshot
Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets
inferredcites →https://doi.org/10.1109/cluster.2019.8891037openalex-snapshot
Improving Performance of Data Dumping with Lossy Compression for Scientific Simulation
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00371openalex-snapshot
Adaptive Posit: Parameter aware numerical format for deep learning inference on the edge
inferredcites →https://doi.org/10.1109/dsn-w.2016.67openalex-snapshot
Software-Defined Error-Correcting Codes
inferredcites →https://doi.org/10.1109/emc2.2018.00012openalex-snapshot
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
inferredcites →https://doi.org/10.1109/fpl.2019.00026openalex-snapshot
Evaluating the Hardware Cost of the Posit Number System
inferredcites →https://doi.org/10.1109/ftxs.2018.00006openalex-snapshot
Improving Application Resilience by Extending Error Correction with Contextual Information
inferredcites →https://doi.org/10.1109/iccd.2018.00057openalex-snapshot
Parameterized Posit Arithmetic Hardware Generator
inferredcites →https://doi.org/10.1109/icecs46596.2019.8965031openalex-snapshot
Novel Arithmetics to Accelerate Machine Learning Classifiers in Autonomous Driving Applications
inferredcites →https://doi.org/10.1109/ieeestd.2019.8766229openalex-snapshot
IEEE Standard for Floating-Point Arithmetic
inferredcites →https://doi.org/10.1109/ipdps.2016.11openalex-snapshot
Fast Error-Bounded Lossy HPC Data Compression with SZ
inferredcites →https://doi.org/10.1109/iscas.2018.8351142openalex-snapshot
Architecture Generator for Type-3 Unum Posit Adder/Subtractor
inferredcites →https://doi.org/10.1109/mcse.2017.29openalex-snapshot
The End of Moore's Law: A New Beginning for Information Technology
inferredcites →https://doi.org/10.1109/southeastcon44009.2020.9249734openalex-snapshot
Impact of Contextual Error Correction Techniques in CLAMR
inferredcites →https://doi.org/10.1109/spacecomp.2019.00011openalex-snapshot
PositNN Framework: Tapered Precision Deep Learning Inference for the Edge
inferredcites →https://doi.org/10.1109/tc.1984.1676475openalex-snapshot
Algorithm-Based Fault Tolerance for Matrix Operations
inferredcites →https://doi.org/10.1109/tc.2020.2985971openalex-snapshot
Evaluations on Deep Neural Networks Training Using Posit Number System
inferredcites →https://doi.org/10.1109/tdsc.2004.2openalex-snapshot
Basic concepts and taxonomy of dependable and secure computing
inferredcites →https://doi.org/10.1109/tvcg.2014.2346458openalex-snapshot
Fixed-Rate Compressed Floating-Point Arrays
inferredcites →https://doi.org/10.1109/vlsid51830.2021.00052openalex-snapshot
An Investigation on Inherent Robustness of Posit Data Representation
inferredcites →https://doi.org/10.1145/2000064.2000108openalex-snapshot
Dark silicon and the end of multicore scaling
inferredcites →https://doi.org/10.1145/227699.227701openalex-snapshot
Handling floating-point exceptions in numeric programs
inferredcites →https://doi.org/10.1145/2694344.2694348openalex-snapshot
Memory Errors in Modern Systems
inferredcites →https://doi.org/10.1145/3078597.3078617openalex-snapshot
Towards a More Complete Understanding of SDC Propagation
inferredcites →https://doi.org/10.1145/3126908.3126937openalex-snapshot
Failures in large scale systems
inferredcites →https://doi.org/10.1145/3316279.3316281openalex-snapshot
Posits as an alternative to floats for weather and climate models
inferredcites →https://doi.org/10.1145/3316279.3316282openalex-snapshot
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks
inferredcites →https://doi.org/10.1145/3387902.3392632openalex-snapshot
Approximating trigonometric functions for posits using the CORDIC method
inferredcites →https://doi.org/10.1177/1094342014522573openalex-snapshot
Addressing failures in exascale computing
inferredcites →https://doi.org/10.1177/1094342019853336openalex-snapshot
Use cases of lossy compression for floating-point data in scientific data sets
inferredcites →https://doi.org/10.14529/jsfi140101openalex-snapshot
Toward Exascale Resilience: 2014 update
inferredcites →https://doi.org/10.14529/jsfi170206openalex-snapshot
Beating Floating Point at its Own Game: Posit Arithmetic
inferredcites →https://doi.org/10.21236/ada605735openalex-snapshot
The RISC-V Instruction Set Manual. Volume 1: User-Level ISA, Version 2.0
inferredcites →https://doi.org/10.2172/1089338openalex-snapshot
Quantifying the Impact of Single Bit Flips on Floating Point Arithmetic
inferredcites →https://doi.org/10.23919/date.2018.8342187openalex-snapshot
Universal number posit arithmetic generator on FPGA
inferredcites →https://doi.org/10.23919/date.2019.8715262openalex-snapshot
Deep Positron: A Deep Neural Network Using the Posit Number System
inferredcites →https://doi.org/10.23919/eeta.2018.8493233openalex-snapshot
Exploiting Posit Arithmetic for Deep Neural Networks in Autonomous Driving Applications
inferredcites →https://doi.org/10.3390/electronics9101622openalex-snapshot
Posit Arithmetic Hardware Implementations with The Minimum Cost Divider and SquareRoot
inferredcites →https://doi.org/10.3390/s20051515openalex-snapshot
Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic
inferredcites →https://doi.org/10.48550/arxiv.1604.03168openalex-snapshot
Hardware-oriented Approximation of Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1609.07061openalex-snapshot
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
inferredcites →https://doi.org/10.48550/arxiv.1711.05852openalex-snapshot
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
inferredcites →https://doi.org/10.48550/arxiv.2012.11011openalex-snapshot
Universal Numbers Library: design and implementation of a high-performance reproducible number systems library
inferredcites →https://doi.org/10.5860/choice.35-2759openalex-snapshot
Elementary functions: algorithms and implementation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1038/s42254-025-00823-7openalex-snapshot
Shifting sands of hardware and software in exascale quantum mechanical simulations
inferred← citeshttps://doi.org/10.1109/iccd65941.2025.00014openalex-snapshot
PolyPE: An Efficient Multi-Precision Multi-Mode Floating-Point Processing Element for HPC and AI
inferred← citeshttps://doi.org/10.1109/scw63240.2024.00077openalex-snapshot
Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs
inferred← citeshttps://doi.org/10.1109/tcad.2023.3316994openalex-snapshot
A Low-Cost Floating-Point Dot-Product-Dual-Accumulate Architecture for HPC-Enabled AI
inferred← citeshttps://doi.org/10.1109/tcsii.2024.3359678openalex-snapshot
A Low-Cost Floating-Point FMA Unit Supporting Package Operations for HPC-AI Applications
inferred← citeshttps://doi.org/10.1109/tetc.2025.3615827openalex-snapshot
Investigating and Mitigating Critical Faults in Floating-Point and Posit Arithmetic Hardware
inferred← citeshttps://doi.org/10.1109/vlsid57277.2023.00059openalex-snapshot
Design and Analysis of Posit Quire Processing Engine for Neural Network Applications
inferred← citeshttps://doi.org/10.1145/3772284openalex-snapshot
Navigating Posit Arithmetic: A Comprehensive Survey of Principles, Hardware, and Applications
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3067704/v1openalex-snapshot
A novel approach to enhance the time for edge computing in IoT by using magic square and error correction techniques
inferred← citeshttps://doi.org/10.3390/math11041055openalex-snapshot
Programming for High-Performance Computing on Edge Accelerators
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