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Resource-Efficient Convolutional Networks: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
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Outgoing claims: the papers this work cites, builds on, or argues with.
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inferredcites →https://doi.org/10.1109/mdat.2017.2741463openalex-snapshot
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inferredcites →https://doi.org/10.1109/mm.2018.112130359openalex-snapshot
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inferredcites →https://doi.org/10.1109/mnnfs.1996.493808openalex-snapshot
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inferredcites →https://doi.org/10.1109/msp.2017.2765695openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbcas.2011.2158314openalex-snapshot
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inferredcites →https://doi.org/10.1109/tbme.2003.820390openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnn.2004.832719openalex-snapshot
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inferredcites →https://doi.org/10.1113/jphysiol.1952.sp004764openalex-snapshot
A quantitative description of membrane current and its application to conduction and excitation in nerve
inferredcites →https://doi.org/10.1137/1.9781611970364openalex-snapshot
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inferredcites →https://doi.org/10.1137/19m1289546openalex-snapshot
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inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
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inferredcites →https://doi.org/10.1145/2463585.2463588openalex-snapshot
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inferredcites →https://doi.org/10.1145/3020078.3021727openalex-snapshot
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inferredcites →https://doi.org/10.1145/503048.503072openalex-snapshot
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inferredcites →https://doi.org/10.1162/neco.1989.1.4.541openalex-snapshot
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inferredcites →https://doi.org/10.1162/neco.1992.4.1.1openalex-snapshot
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inferredcites →https://doi.org/10.3389/fnins.2016.00333openalex-snapshot
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inferredcites →https://doi.org/10.3389/fnins.2017.00538openalex-snapshot
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inferredcites →https://doi.org/10.5244/c.29.160openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tc.2026.3703350openalex-snapshot
Mitigating Scalability Challenges in LUT-Based Neural Networks via Pruning Optimisations
inferred← citeshttps://doi.org/10.1109/tcad.2025.3576320openalex-snapshot
SPLAT: Revisiting Latency Attack on Dynamic Neural Networks
inferred← citeshttps://doi.org/10.1109/tcasai.2025.3569511openalex-snapshot
A Configurable Floating-Point Fused Multiply-Add Design With Mixed Precision for AI Accelerators
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3430028openalex-snapshot
On-Device Deep Learning: Survey on Techniques Improving Energy Efficiency of DNNs
inferred← citeshttps://doi.org/10.1109/tnse.2025.3595747openalex-snapshot
Progress and Bandwidth Aware Partial Model Synchronization With Selective Reduce
inferred← citeshttps://doi.org/10.1145/3672758.3672817openalex-snapshot
Optimization of Convolution Operators' Backpropagation for Domestic Accelerators: Convolution Operators: Convolution kernel operation Backpropagation optimization to Enhance Performance and Efficiency in Convolutional Neural Network
inferred← citeshttps://doi.org/10.1145/3697090.3697092openalex-snapshot
Allocating ECC parity bits into BF16-encoded CNN parameters: A practical experience report
inferred← citeshttps://doi.org/10.1145/3724391openalex-snapshot
Domain Generalization in Computational Pathology: Survey and Guidelines
inferred← citeshttps://doi.org/10.1145/3786659openalex-snapshot
IDAP++: Advancing Divergence-Aware Pruning with Joint Filter and Layer Optimization
inferred← citeshttps://doi.org/10.14569/ijacsa.2024.0150744openalex-snapshot
Children's Expression Recognition Based on Multi-Scale Asymmetric Convolutional Neural Network
inferred← citeshttps://doi.org/10.31577/cai_2025_1_1openalex-snapshot
Resource-Efficient Model for Deep Kernel Learning
inferred← citeshttps://doi.org/10.3390/electronics14040657openalex-snapshot
VCONV: A Convolutional Neural Network Accelerator for FPGAs
inferred← citeshttps://doi.org/10.3390/mi15030343openalex-snapshot
Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study
inferred← citeshttps://doi.org/10.36227/techrxiv.174952895.55368377/v1openalex-snapshot
Towards Interdisciplinary Energy-Efficient Computing for the Post-Transistor Scaling Era
inferred← citeshttps://doi.org/10.47749/t/unicamp.2022.1251262openalex-snapshot
Flexible collaborative inference for computer vision
inferred← citeshttps://doi.org/10.5909/jbe.2024.29.3.291openalex-snapshot
Low-Complexity Vision Transformer with Adaptive Channel Partitioning Method
inferred← citeshttps://doi.org/10.6000/1929-6029.2026.15.15openalex-snapshot
Real-Time Blood Pressure Change Classification Using Enhanced PPG Signals and Convolutional Neural Networks
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