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

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Showing 2251–2300 of 4925 papers

TitleStatusHype
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge—0
Dequantization of a signal from two parallel quantized observations—0
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval—0
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization—0
KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
BasisConv: A method for compressed representation and learning in CNNs—0
Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)—0
Amenable Sparse Network Investigator—0
Bang for the Buck: Vector Search on Cloud CPUs—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Demystifying Singular Defects in Large Language Models—0
Bandwidth-efficient Inference for Neural Image Compression—0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM—0
Demystifying and Generalizing BinaryConnect—0
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity—0
DeltaDQ: Ultra-High Delta Compression for Fine-Tuned LLMs via Group-wise Dropout and Separate Quantization—0
Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
Defensive Quantization: When Efficiency Meets Robustness—0
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model—0
Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection—0
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
KurTail : Kurtosis-based LLM Quantization—0
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming—0
Deep Unfolding with Kernel-based Quantization in MIMO Detection—0
Balance of Number of Embedding and their Dimensions in Vector Quantization—0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks—0
Alternating Multi-bit Quantization for Recurrent Neural Networks—0
Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning—0
Deep Task-Based Quantization—0
Deep Spherical Quantization for Image Search—0
Bag of Tricks with Quantized Convolutional Neural Networks for image classification—0
Alternating Direction Method of Multipliers for Quantization—0
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems—0
Deep Signal Recovery with One-Bit Quantization—0
Alternating Co-Quantization for Cross-Modal Hashing—0
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes—0
Deep Saliency Hashing—0
Back to Simplicity: How to Train Accurate BNNs from Scratch?—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

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