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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/biocas.2015.7348372openalex-snapshot
Utilizing deep neural nets for an embedded ECG-based biometric authentication system
inferredcites →https://doi.org/10.1109/biocas.2015.7348376openalex-snapshot
A low power seizure detection processor based on direct use of compressively-sensed data and employing a deterministic random matrix
inferredcites →https://doi.org/10.1109/bsn.2015.7299406openalex-snapshot
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities
inferredcites →https://doi.org/10.1109/ccgrid21080.2013openalex-snapshot
2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvprw.2014.106openalex-snapshot
A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks
inferredcites →https://doi.org/10.1109/fccm.2016.58openalex-snapshot
FPGA-Based Reduction Techniques for Efficient Deep Neural Network Deployment
inferredcites →https://doi.org/10.1109/fccm53951.2022openalex-snapshot
2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
inferredcites →https://doi.org/10.1109/hotchips.2016.7936226openalex-snapshot
Deep compression and EIE: Efficient inference engine on compressed deep neural network
inferredcites →https://doi.org/10.1109/iccd.2013.6657019openalex-snapshot
Memory-centric accelerator design for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/isca.2016.30openalex-snapshot
EIE: Efficient Inference Engine on Compressed Deep Neural Network
inferredcites →https://doi.org/10.1109/iscas.2016.7527433openalex-snapshot
Wearable seizure detection using convolutional neural networks with transfer learning
inferredcites →https://doi.org/10.1109/isscc.2016.7418003openalex-snapshot
14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses
inferredcites →https://doi.org/10.1109/isscc.2016.7418007openalex-snapshot
14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
inferredcites →https://doi.org/10.1109/isscc.2016.7418008openalex-snapshot
14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems
inferredcites →https://doi.org/10.1109/jssc.2014.2356197openalex-snapshot
A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS
inferredcites →https://doi.org/10.1109/jssc.2016.2616357openalex-snapshot
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tbcas.2015.2504563openalex-snapshot
An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications
inferredcites →https://doi.org/10.1109/tcsii.2014.2385211openalex-snapshot
A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection
inferredcites →https://doi.org/10.1109/tii.2013.2294137openalex-snapshot
FPGA Implementation of the C-Mantec Neural Network Constructive Algorithm
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/1815961.1815993openalex-snapshot
A dynamically configurable coprocessor for convolutional neural networks
inferredcites →https://doi.org/10.1145/2684746.2689060openalex-snapshot
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/2847263.2847265openalex-snapshot
Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
inferredcites →https://doi.org/10.1145/2847263.2847276openalex-snapshot
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1310.6343openalex-snapshot
Provable Bounds for Learning Some Deep Representations
inferredcites →https://doi.org/10.48550/arxiv.1412.6806openalex-snapshot
Striving for Simplicity: The All Convolutional Net
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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/s00034-020-01534-3openalex-snapshot
M2DA: A Low-Complex Design Methodology for Convolutional Neural Network Exploiting Data Symmetry and Redundancy
inferred← citeshttps://doi.org/10.1007/s00521-018-3761-1openalex-snapshot
A survey of FPGA-based accelerators for convolutional neural networks
inferred← citeshttps://doi.org/10.1007/s11063-021-10458-1openalex-snapshot
A Survey of Algorithmic and Hardware Optimization Techniques for Vision Convolutional Neural Networks on FPGAs
inferred← citeshttps://doi.org/10.1007/s11554-026-01954-5openalex-snapshot
Fb-FaceBoxes: FPGA-based face detection using repetitive kernels and memory-access optimization
inferred← citeshttps://doi.org/10.1016/j.dsp.2026.106402openalex-snapshot
Consistency-aware structural modeling of time-series signals via learnable spectral-image transform
inferred← citeshttps://doi.org/10.1016/j.vlsi.2019.07.005openalex-snapshot
Computer vision algorithms and hardware implementations: A survey
inferred← citeshttps://doi.org/10.1088/1674-4926/41/2/021403openalex-snapshot
A survey of neural network accelerator with software development environments
inferred← citeshttps://doi.org/10.1109/access.2024.3425166openalex-snapshot
A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications
inferred← citeshttps://doi.org/10.1109/coniiti.2018.8587095openalex-snapshot
A Smart Classroom Based on Deep Learning and Osmotic IoT Computing
inferred← citeshttps://doi.org/10.1109/eit.2018.8500260openalex-snapshot
Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices
inferred← citeshttps://doi.org/10.1109/fccm.2017.55openalex-snapshot
A Real-Time Embedded FPGA Processor for a Stand-Alone Dual-Mode Assistive Device
inferred← citeshttps://doi.org/10.1109/fccm.2017.56openalex-snapshot
A Scalable FPGA-Based Accelerator for High-Throughput MCMC Algorithms
inferred← citeshttps://doi.org/10.1109/hipc.2019.00033openalex-snapshot
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
inferred← citeshttps://doi.org/10.1109/icdcs54290.2022.9780689openalex-snapshot
Review of FPGA-Based Accelerators of Deep Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/icpr48806.2021.9413182openalex-snapshot
Activation Density Driven Efficient Pruning in Training
inferred← citeshttps://doi.org/10.1109/iscas.2017.8050342openalex-snapshot
PACENet: Energy efficient acceleration for convolutional network on embedded platform
inferred← citeshttps://doi.org/10.1109/iscas.2017.8050588openalex-snapshot
Accelerating convolutional neural network with FFT on tiny cores
inferred← citeshttps://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00099openalex-snapshot
WGeod: A General and Efficient FPGA Accelerator for Object Detection
inferred← citeshttps://doi.org/10.1109/isqed.2019.8697574openalex-snapshot
Minimizing Classification Energy of Binarized Neural Network Inference for Wearable Devices
inferred← citeshttps://doi.org/10.1109/itaic64559.2025.11163048openalex-snapshot
Efficient Modulation Format Identification Engine with CNN on FPGA
inferred← citeshttps://doi.org/10.1109/jetcas.2021.3129415openalex-snapshot
A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference
inferred← citeshttps://doi.org/10.1109/jiot.2022.3172843openalex-snapshot
Power-Efficient Implementation of Ternary Neural Networks in Edge Devices
inferred← citeshttps://doi.org/10.1109/jproc.2021.3098483openalex-snapshot
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
inferred← citeshttps://doi.org/10.1109/ojcas.2020.3043737openalex-snapshot
An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/ojcas.2021.3123899openalex-snapshot
Implementing Convolutional Neural Networks Using Hartley Stochastic Computing With Adaptive Rate Feature Map Compression
inferred← citeshttps://doi.org/10.1109/tbcas.2017.2757031openalex-snapshot
A Low-Power Wearable Stand-Alone Tongue Drive System for People With Severe Disabilities
inferred← citeshttps://doi.org/10.1109/tcad.2020.2968023openalex-snapshot
DSP-Efficient Hardware Acceleration of Convolutional Neural Network Inference on FPGAs
inferred← citeshttps://doi.org/10.1109/tcsi.2018.2848647openalex-snapshot
SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification
inferred← citeshttps://doi.org/10.1109/tvlsi.2017.2754272openalex-snapshot
An Energy-Efficient Programmable Manycore Accelerator for Personalized Biomedical Applications
inferred← citeshttps://doi.org/10.1109/tvlsi.2018.2825145openalex-snapshot
Accelerating Convolutional Neural Network With FFT on Embedded Hardware
inferred← citeshttps://doi.org/10.1145/3092944openalex-snapshot
Low Overhead CS-Based Heterogeneous Framework for Big Data Acceleration
inferred← citeshttps://doi.org/10.1145/3358699openalex-snapshot
Heterogeneous Scheduling of Deep Neural Networks for Low-power Real-time Designs
inferred← citeshttps://doi.org/10.1145/3373087.3375302openalex-snapshot
Reuse Kernels or Activations?
inferred← citeshttps://doi.org/10.1145/3423136openalex-snapshot
Binary Precision Neural Network Manycore Accelerator
inferred← citeshttps://doi.org/10.1145/3613963openalex-snapshot
FPGA-based Deep Learning Inference Accelerators: Where Are We Standing?
inferred← citeshttps://doi.org/10.3390/ai5030064openalex-snapshot
Sustainable Machine Vision for Industry 4.0: A Comprehensive Review of Convolutional Neural Networks and Hardware Accelerators in Computer Vision
inferred← citeshttps://doi.org/10.48550/arxiv.2002.02949openalex-snapshot
Activation Density driven Energy-Efficient Pruning in Training
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