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 251–275 of 4925 papers

TitleStatusHype
MAUVE Scores for Generative Models: Theory and PracticeCode2
Compressing Volumetric Radiance Fields to 1 MBCode2
A Closer Look at Hardware-Friendly Weight QuantizationCode2
I-ViT: Integer-only Quantization for Efficient Vision Transformer InferenceCode2
On-Device Training Under 256KB MemoryCode2
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale TransformersCode2
Re-parameterizing Your Optimizers rather than ArchitecturesCode2
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel DecoderCode2
BMInf: An Efficient Toolkit for Big Model Inference and TuningCode2
4-bit Conformer with Native Quantization Aware Training for Speech RecognitionCode2
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training QuantizationCode2
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and NormalizationCode2
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning DevicesCode2
Fast convolutional neural networks on FPGAs with hls4mlCode2
FBGEMM: Enabling High-Performance Low-Precision Deep Learning InferenceCode2
I-BERT: Integer-only BERT QuantizationCode2
Binary Neural Networks: A SurveyCode2
Neural Network Compression Framework for fast model inferenceCode2
HAQ: Hardware-Aware Automated Quantization with Mixed PrecisionCode2
Bolt: Accelerated Data Mining with Fast Vector CompressionCode2
Compress Any Segment Anything Model (SAM)Code1
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image TranslationCode1
Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language ModelsCode1
CommVQ: Commutative Vector Quantization for KV Cache CompressionCode1
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix ApproximationCode1
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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