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 3576–3600 of 4925 papers

TitleStatusHype
Analytical aspects of non-differentiable neural networks—0
Analyzing Compression Techniques for Computer Vision—0
Analyzing Quantization in TVM—0
An Analysis on Quantizing Diffusion Transformers—0
An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM—0
An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis—0
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks—0
An Efficient Index for Visual Search in Appearance-based SLAM—0
An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback—0
An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes—0
An Empirical Study of Low Precision Quantization for TinyML—0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC—0
A "Network Pruning Network" Approach to Deep Model Compression—0
A new heuristic algorithm for fast k-segmentation—0
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip—0
A New Old Idea: Beam-Steering Reflectarrays for Efficient Sub-THz Multiuser MIMO—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration—0
An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits—0
Angle Estimation of a Single Source with Massive Uniform Circular Arrays—0
Angular Quantization-based Binary Codes for Fast Similarity Search—0
An Implementation of Vector Quantization using the Genetic Algorithm Approach—0
An Improved BKW Algorithm for LWE with Applications to Cryptography and Lattices—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