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 2326–2350 of 4925 papers

TitleStatusHype
Is Conventional SNN Really Efficient? A Perspective from Network Quantization—0
A Speed Odyssey for Deployable Quantization of LLMs—0
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs QuantizationCode0
On the Impact of Calibration Data in Post-training Quantization and Pruning—0
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video—0
Data Augmentations in Deep Weight Spaces—0
MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization—0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation RelaxingCode0
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling—0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline—0
Compressed and Sparse Models for Non-Convex Decentralized Learning—0
Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization—0
Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks—0
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures—0
Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN—0
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models—0
Deep Hashing via Householder QuantizationCode0
Generative Diffusion Models for Lattice Field Theory—0
Learned layered coding for Successive Refinement in the Wyner-Ziv Problem—0
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency—0
Effective Quantization for Diffusion Models on CPUs—0
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization—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