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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 4925 papers

TitleStatusHype
Imp: Highly Capable Large Multimodal Models for Mobile DevicesCode2
On-Device Training Under 256KB MemoryCode2
Binarized Neural Machine TranslationCode2
Compressing Volumetric Radiance Fields to 1 MBCode2
MobileQuant: Mobile-friendly Quantization for On-device Language ModelsCode2
Palu: Compressing KV-Cache with Low-Rank ProjectionCode2
HAQ: Hardware-Aware Automated Quantization with Mixed PrecisionCode2
Harmonizing Visual Representations for Unified Multimodal Understanding and GenerationCode2
PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models QuantizationCode2
Preventing Local Pitfalls in Vector Quantization via Optimal TransportCode2
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning DevicesCode2
GuidedQuant: Large Language Model Quantization via Exploiting End Loss GuidanceCode2
QAQ: Quality Adaptive Quantization for LLM KV CacheCode2
An empirical study of LLaMA3 quantization: from LLMs to MLLMsCode2
GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook RetrievalCode2
Atom: Low-bit Quantization for Efficient and Accurate LLM ServingCode2
GENIUS: A Generative Framework for Universal Multimodal SearchCode2
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image GenerationCode2
GaussianToken: An Effective Image Tokenizer with 2D Gaussian SplattingCode2
decoupleQ: Towards 2-bit Post-Training Uniform Quantization via decoupling Parameters into Integer and Floating PointsCode2
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space ModelsCode2
Quamba: A Post-Training Quantization Recipe for Selective State Space ModelsCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inferenceCode2
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLMCode2
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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