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 2826–2850 of 4925 papers

TitleStatusHype
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time—0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization—0
Soft Label Coding for End-to-end Sound Source Localization With Ad-hoc Microphone Arrays—0
SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on Associative Processors—0
Softmax Bias Correction for Quantized Generative Models—0
Soft then Hard: Rethinking the Quantization in Neural Image Compression—0
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations—0
Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces—0
Solving Multi-Arm Bandit Using a Few Bits of Communication—0
Some Further Evidence about Magnification and Shape in Neural Gas—0
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
Some useful approximations for calculation of directivities of multibeam power patterns of large planar arrays—0
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model—0
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing—0
SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms—0
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization—0
Sparse*BERT: Sparse Models Generalize To New tasks and Domains—0
Sparse Composite Quantization—0
Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search—0
Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity—0
Sparse linear regression with compressed and low-precision data via concave quadratic programming—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