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 2926–2950 of 4925 papers

TitleStatusHype
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition—0
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach—0
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets—0
Subgraph Stationary Hardware-Software Inference Co-Design—0
SUBIC: A supervised, structured binary code for image search—0
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display—0
Sublinear quantum algorithms for training linear and kernel-based classifiers—0
Subspace Robust Wasserstein Distances—0
Subtensor Quantization for Mobilenets—0
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime—0
Sum Rate Maximization in the Constant Envelope MIMO Downlink with the RZF Precoder—0
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization—0
Super-relaxation of space-time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation—0
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images—0
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning—0
Supervised Learning in the Presence of Concept Drift: A modelling framework—0
Supervised Matrix Factorization for Cross-Modality Hashing—0
Supervised Quantization for Similarity Search—0
Support Recovery in Universal One-bit Compressed Sensing—0
Survey of Quantization Techniques for On-Device Vision-based Crack Detection—0
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency—0
SUT System Description for Anti-Spoofing 2017 Challenge—0
SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention—0
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers—0
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration—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