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 2401–2425 of 4925 papers

TitleStatusHype
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Efficient Compression of Multitask Multilingual Speech Models—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
Efficient Channel Estimator with Angle-Division Multiple Access—0
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression—0
An NMF Perspective on Binary Hashing—0
Efficient Bitwidth Search for Practical Mixed Precision Neural Network—0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization—0
Efficient Asynchronous Federated Learning with Sparsification and Quantization—0
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)—0
AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding—0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification—0
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU—0
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores—0
Efficient ANN-SNN Conversion with Error Compensation Learning—0
Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
Anisotropic oracle inequalities in noisy quantization—0
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference—0
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines—0
Efficient and accurate neural field reconstruction using resistive memory—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—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