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 2251–2275 of 4925 papers

TitleStatusHype
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks—0
Energy awareness in low precision neural networks—0
Energy-Aware LLMs: A step towards sustainable AI for downstream applications—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml—0
Characterizing Coherent Integrated Photonic Neural Networks under Imperfections—0
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression—0
End-to-end Quantized Training via Log-Barrier Extensions—0
End-to-End Optimized Speech Coding with Deep Neural Networks—0
Characterization of the frequency response of channel-interleaved photonic ADCs based on the optical time-division demultiplexer—0
APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech—0
Adaptive Quantization for Deep Neural Network—0
Accelerating Deep Learning Inference via Freezing—0
End-to-end optimization of nonlinear transform codes for perceptual quality—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
End-to-end Learning of Compressible Features—0
Characterising Bias in Compressed Models—0
A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications—0
End-to-End Learned Image Compression with Quantized Weights and Activations—0
End-to-End Latent Fingerprint Search—0
End-to-end Keyword Spotting using Neural Architecture Search and Quantization—0
Channel-Wise Mixed-Precision Quantization for Large Language Models—0
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm—0
End-to-end fully-binarized network design: from Generic Learned Thermometer to Block Pruning—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—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