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 1951–1975 of 4925 papers

TitleStatusHype
A Reconstruction-Computation-Quantization (RCQ) Approach to Node Operations in LDPC Decoding—0
A Deep Hashing Learning Network—0
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning—0
Generative Design of Hardware-aware DNNs—0
Generative Diffusion Models for Lattice Field Theory—0
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference—0
Generative QoE Modeling: A Lightweight Approach for Telecom Networks—0
Generative Semantic Communication for Text-to-Speech Synthesis—0
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC—0
Fast Orthogonal Projection Based on Kronecker Product—0
Fast on-line signature recognition based on VQ with time modeling—0
A Reconfigurable Dual-Mode Tracking SAR ADC without Analog Subtraction—0
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions—0
Geometry and clustering with metrics derived from separable Bregman divergences—0
3D Pathfinding and Collision Avoidance Using Uneven Search-space Quantization and Visual Cone Search—0
Getting Free Bits Back from Rotational Symmetries in LLMs—0
Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification—0
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes—0
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression—0
Fast learning rates with heavy-tailed losses—0
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent—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