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 4051–4075 of 4925 papers

TitleStatusHype
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Generalization Error Analysis of Quantized Compressive Learning—0
Random Projections with Asymmetric Quantization—0
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic—0
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable QuantizationCode1
Coresets for Archetypal AnalysisCode0
Normalization Helps Training of Quantized LSTMCode0
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization—0
QKD: Quantization-aware Knowledge Distillation—0
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices—0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
Model-Aware Deep Architectures for One-Bit Compressive Variational AutoencodingCode0
Music Source Separation in the Waveform Domain—0
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation—0
Pyramid Vector Quantization and Bit Level Sparsity in Weights for Efficient Neural Networks Inference—0
Quantization NetworksCode0
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech—0
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers—0
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep LearningCode0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
Online Learned Continual Compression with Adaptive Quantization ModulesCode1
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUsCode0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—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