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 526–550 of 4925 papers

TitleStatusHype
One-Bit Sigma-Delta DFRC Waveform Design: Using Quantization Noise for Radar Probing—0
SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
GaussianToken: An Effective Image Tokenizer with 2D Gaussian SplattingCode2
FBQuant: FeedBack Quantization for Large Language Models—0
RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations—0
AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models—0
Channel-Aware Constellation Design for Digital OTA Computation—0
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml—0
On Hardening DNNs against Noisy Computations—0
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution FittingCode2
Qrazor: Reliable and effortless 4-bit llm quantization by significant data razoring—0
QMamba: Post-Training Quantization for Vision State Space Models—0
MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods—0
Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse—0
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
Quantized Spike-driven TransformerCode1
HEPPO: Hardware-Efficient Proximal Policy Optimization -- A Universal Pipelined Architecture for Generalized Advantage Estimation—0
Irrational Complex Rotations Empower Low-bit Optimizers—0
Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes—0
GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language ModelsCode0
SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization—0
HAC++: Towards 100X Compression of 3D Gaussian SplattingCode3
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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