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 2401–2425 of 4925 papers

TitleStatusHype
Deep Neural Network Models Compression—0
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition—0
Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry—0
Just CHOP: Embarrassingly Simple LLM Compression—0
Large Language Models For Text Classification: Case Study And Comprehensive Review—0
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models—0
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
LAST: Language Model Aware Speech Tokenization—0
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks—0
Lattice Functions for the Analysis of Analog-to-Digital Conversion—0
Lattice Quantization—0
Lattice Representation Learning—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors—0
Learning with tree tensor networks: complexity estimates and model selection—0
Layer-wise Quantization for Quantized Optimistic Dual Averaging—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
LCQ: Low-Rank Codebook based Quantization for Large Language Models—0
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT—0
LDPC Decoding with Degree-Specific Neural Message Weights and RCQ Decoding—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation—0
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks—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