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 2476–2500 of 4925 papers

TitleStatusHype
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models—0
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge—0
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models—0
CHIME: A Compressive Framework for Holistic Interest Modeling—0
Choose Your Model Size: Any Compression by a Single Gradient Descent—0
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech—0
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer—0
Class-based Quantization for Neural Networks—0
Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
Clustering with Bregman Divergences: an Asymptotic Analysis—0
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss—0
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Cluster Regularized Quantization for Deep Networks Compression—0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems—0
CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture—0
CNN inference acceleration using dictionary of centroids—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