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 551–575 of 4925 papers

TitleStatusHype
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
Practical Modulo Sampling: Mitigating High-Frequency Components—0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching—0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks—0
Ditto: Accelerating Diffusion Model via Temporal Value Similarity—0
BeST -- A Novel Source Selection Metric for Transfer Learning—0
LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR dataCode0
DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization—0
A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation—0
LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator—0
4bit-Quantization in Vector-Embedding for RAGCode0
Lossless Compression of Vector IDs for Approximate Nearest Neighbor SearchCode2
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning—0
Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever—0
Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach—0
Large Language Models For Text Classification: Case Study And Comprehensive Review—0
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems—0
Dataset Distillation as Pushforward Optimal Quantization—0
QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision Quantization for Practical Embedded AI Applications—0
FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices—0
ZOQO: Zero-Order Quantized Optimization—0
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy TheoryCode0
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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