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 2051–2100 of 4925 papers

TitleStatusHype
Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Fast binary embeddings, and quantized compressed sensing with structured matrices—0
Fast Autoregressive Models for Continuous Latent Generation—0
Communication Efficient Federated Learning over Multiple Access Channels—0
Quantum Block-Matching Algorithm using Dissimilarity Measure—0
Communication-Efficient Federated Distillation—0
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences—0
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization—0
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons—0
FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs--Down to 2 Bits!—0
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM—0
Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach—0
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization—0
3DMolNet: A Generative Network for Molecular Structures—0
False Detection (Positives and Negatives) in Object Detection—0
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control—0
FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints—0
A Quantization-based Technique for Privacy Preserving Distributed Learning—0
Factorized Visual Tokenization and Generation—0
Face recognition using color local binary pattern from mutually independent color channels—0
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation—0
Communication Compression for Tensor Parallel LLM Inference—0
A Quantitative Approach To The Temporal Dependency in Video Coding—0
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM—0
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique—0
Extreme Image Compression using Fine-tuned VQGANs—0
COMET: Towards Partical W4A4KV4 LLMs Serving—0
Extreme Compression for Pre-trained Transformers Made Simple and Efficient—0
Post Training Quantization of Large Language Models with Microscaling Formats—0
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising—0
Exposing Hardware Building Blocks to Machine Learning Frameworks—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
A QP-adaptive Mechanism for CNN-based Filter in Video Coding—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Exploring Semantic Segmentation on the DCT Representation—0
Collaborative Quantization for Cross-Modal Similarity Search—0
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration—0
Exploring Neural Networks Quantization via Layer-Wise Quantization Analysis—0
Exploring Model Invariance with Discrete Search for Ultra-Low-Bit Quantization—0
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks—0
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models—0
A Data and Compute Efficient Design for Limited-Resources Deep Learning—0
Exploring FPGA designs for MX and beyond—0
Exploring Extreme Quantization in Spiking Language Models—0
Collaborative Filtering with Smooth Reconstruction of the Preference Function—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
Collaborative Edge AI Inference over Cloud-RAN—0
Explore the Potential of CNN Low Bit Training—0
Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming—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