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 1126–1150 of 4925 papers

TitleStatusHype
Distributed Learning with Compressed Gradient Differences—0
Comparison of 14 different families of classification algorithms on 115 binary datasets—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Are disentangled representations all you need to build speaker anonymization systems?—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation—0
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding—0
A Deep Hashing Learning Network—0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC—0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction—0
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions—0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Distributed Learning with Sublinear Communication—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