SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Showing 11011150 of 4925 papers

TitleStatusHype
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification0
ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM0
ADMM Based Semi-Structured Pattern Pruning Framework For Transformer0
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond0
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case0
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing0
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design0
A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits0
Distributed CPU Scheduling Subject to Nonlinear Constraints0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation0
Composite Correlation Quantization for Efficient Multimodal Retrieval0
Composite Code Sparse Autoencoders for first stage retrieval0
Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition0
Component Training of Turbo Autoencoders0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners0
A Review of Recent Advances of Binary Neural Networks for Edge Computing0
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other0
A review of learning vector quantization classifiers0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation0
Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks0
Distributed Learning with Compressed Gradient Differences0
Comparison of 14 different families of classification algorithms on 115 binary datasets0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling0
High-performance deep spiking neural networks with 0.3 spikes per neuron0
Comparing Fisher Information Regularization with Distillation for DNN Quantization0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers0
Accelerator-Aware Training for Transducer-Based Speech Recognition0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking0
Compact Representation for Image Classification: To Choose or to Compress?0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms0
Are disentangled representations all you need to build speaker anonymization systems?0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization0
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search0
Compact Neural Graphics Primitives with Learned Hash Probing0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation0
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding0
A Deep Hashing Learning Network0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction0
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters0
Distributed Learning with Sublinear Communication0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified