Reference health
A Systematic Study of Tiny YOLO3 Inference: Toward Compact Brainware Processor With Less Memory and Logic Gate
60/60 checkable references clean · checked 2026-07-24
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
28 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 60 checked references that resolve
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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey
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Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
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An Analytical Framework for Evaluating the Error Characteristics of Approximate Adders
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Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
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YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration
resolves10.1109/JSSC.2019.2930910
A 47.14-$\mu\text{W}$ 200-MHz MOS/MTJ-Hybrid Nonvolatile Microcontroller Unit Embedding STT-MRAM and FPGA for IoT Applications
resolves10.1109/JPROC.2016.2574939
Standby-Power-Free Integrated Circuits Using MTJ-Based VLSI Computing
resolves10.1109/JETCAS.2019.2914355
iFPNA: A Flexible and Efficient Deep Learning Processor in 28-nm CMOS Using a Domain-Specific Instruction Set and Reconfigurable Fabric
resolves10.1007/978-3-319-46493-0_32
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
resolves10.1109/TVLSI.2019.2905242
A High-Throughput and Power-Efficient FPGA Implementation of YOLO CNN for Object Detection
resolves10.1109/TII.2018.2822828
Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment
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Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
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Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks
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A Fixed-Point Quantization Technique for Convolutional Neural Networks Based on Weight Scaling
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A 2.6 TOPS/W 16-Bit Fixed-Point Convolutional Neural Network Learning Processor in 65-nm CMOS
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
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Pruning Blocks for CNN Compression and Acceleration via Online Ensemble Distillation
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In-Hardware Training Chip Based on CMOS Invertible Logic for Machine Learning
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Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems
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Towards artificial general intelligence with hybrid Tianjic chip architecture
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FPGA Implementation of Real-Time Pedestrian Detection Using Normalization-Based Validation of Adaptive Features Clustering
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Analysis, Modeling and Optimization of Equal Segment Based Approximate Adders
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A Review, Classification, and Comparative Evaluation of Approximate Arithmetic Circuits
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Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancer
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Design of Low-Power High-Speed Truncation-Error-Tolerant Adder and Its Application in Digital Signal Processing
resolves10.1016/j.mejo.2018.10.005
Design of MTJ-Based nonvolatile logic gates for quantized neural networks
resolves10.1109/TCSI.2009.2027626
Bio-Inspired Imprecise Computational Blocks for Efficient VLSI Implementation of Soft-Computing Applications
resolves10.1109/TCSII.2015.2456372
A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses and <italic>In Situ</italic> Learning
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ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
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The 28 references without a DOI — listed, not checked
no DOI — not checkedImproved knowledge distillation via teacher assistant
no DOI — not checkedBorn-again neural networks
no DOI — not checkedPaying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
no DOI — not checkedref77
no DOI — not checkedA Survey of Techniques for Approximate Computing
no DOI — not checkedTernary weight networks
no DOI — not checkedBinaryConnect: Training deep neural networks with binary weights during propagations
no DOI — not checkedUsing code perforation to improve performance, reduce energy consumption, respond to failures
no DOI — not checkedPerforatedCNNs: Acceleration through elimination of redundant convolutions
no DOI — not checkedDistilling the knowledge in a neural network
no DOI — not checkedModel compression
no DOI — not checkedKnowledge distillation for optimization of quantized deep neural networks
no DOI — not checkedFitNets: Hints for thin deep nets
no DOI — not checkedFaster R-CNN: Towards real-time object detection with region proposal networks
no DOI — not checkedYOLOv3: An incremental improvement
no DOI — not checkedYOLOv4: Optimal speed and accuracy of object detection
no DOI — not checkedBinarized neural networks
no DOI — not checkedResiliency of deep neural networks under quantization
no DOI — not checkedA survey on methods and theories of quantized neural networks
no DOI — not checkedImagenet classification with deep convolutional neural networks
no DOI — not checkedAn enhanced low-power high-speed adder for error-tolerant application
no DOI — not checkedComparative study of CNN and RNN for natural language processing
no DOI — not checkedDynamic channel pruning: Feature boosting and suppression
no DOI — not checkedref87
no DOI — not checked5nm CMOS production technology platform featuring full-fledged EUV, and high mobility channel FinFETs with densest $0.021~\mu$
m2 SRAM cells for mobile SoC and high performance computing applications
no DOI — not checkedWhat is the state of neural network pruning?
no DOI — not checkedDeep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
no DOI — not checkedOptimal brain damage
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