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 2201–2250 of 4925 papers

TitleStatusHype
Reducing Communication for Split Learning by Randomized Top-k Sparsification—0
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
LLM-QAT: Data-Free Quantization Aware Training for Large Language ModelsCode3
BRICS: Bi-level feature Representation of Image CollectionS—0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking—0
Disentanglement via Latent QuantizationCode1
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing—0
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals—0
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time—0
2-bit Conformer quantization for automatic speech recognition—0
PQA: Exploring the Potential of Product Quantization in DNN Hardware AccelerationCode0
NVTC: Nonlinear Vector Transform CodingCode1
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range MultilaterationCode1
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Just CHOP: Embarrassingly Simple LLM Compression—0
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers—0
QLoRA: Efficient Finetuning of Quantized LLMsCode6
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image GenerationCode1
Adversarial Defenses via Vector Quantization—0
Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Differential Privacy with Random Projections and Sign Random Projections—0
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers—0
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization—0
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer—0
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference PipelineCode1
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—0
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization—0
Atomic Anatomy of Low-Inertia Power Systems—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
Bi-ViT: Pushing the Limit of Vision Transformer Quantization—0
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes—0
Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector QuantizationCode1
ReTAG: Reasoning Aware Table to Analytic Text Generation—0
PTQD: Accurate Post-Training Quantization for Diffusion ModelsCode1
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization—0
QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture GenerationCode1
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognition—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural NetworksCode0
Component Training of Turbo Autoencoders—0
Fast Inference of Tree Ensembles on ARM Devices—0
Task-Oriented Communication Design at Scale—0
Designing Discontinuities—0
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—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