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 4551–4575 of 4925 papers

TitleStatusHype
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines—0
Mad Max: Affine Spline Insights into Deep Learning—0
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks—0
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks—0
Discrete Factorization Machines for Fast Feature-based RecommendationCode0
Quantization Mimic: Towards Very Tiny CNN for Object Detection—0
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker VerificationCode0
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks—0
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data—0
Information and Statistical Efficiency When Quantizing Noisy DC Values—0
Quantized Compressive K-Means—0
Dynamic Signal Measurements Based on Quantized Data—0
Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image—0
Deep Convolutional AutoEncoder-based Lossy Image CompressionCode0
Single-User mmWave Massive MIMO: SVD-based ADC Bit Allocation and Combiner Design—0
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server—0
On the Impact of Fixed Point Hardware for Optical Fiber Nonlinearity Compensation Algorithms—0
Value-aware Quantization for Training and Inference of Neural Networks—0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case—0
Efficient Channel Estimator with Angle-Division Multiple Access—0
Pieces of Eight: 8-bit Neural Machine Translation—0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Discriminative Cross-View Binary Representation Learning—0
A Compressed Sensing Approach for Distribution Matching—0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
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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