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 1851–1875 of 4925 papers

TitleStatusHype
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing—0
Autoregressive Speech Synthesis without Vector Quantization—0
Applying generative neural networks for fast simulations of the ALICE (CERN) experimentCode0
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers—0
Ternary Spike-based Neuromorphic Signal Processing System—0
Quantizing YOLOv7: A Comprehensive Study—0
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT—0
ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters—0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Balance of Number of Embedding and their Dimensions in Vector Quantization—0
Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation MapsCode0
Hybrid Receiver Design for Massive MIMO-OFDM with Low-Resolution ADCs and Oversampling—0
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models—0
Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs—0
Timestep-Aware Correction for Quantized Diffusion Models—0
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
How Does Quantization Affect Multilingual LLMs?—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
GPTQT: Quantize Large Language Models Twice to Push the Efficiency—0
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation—0
OSPC: Artificial VLM Features for Hateful Meme Detection—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