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 1676–1700 of 4925 papers

TitleStatusHype
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Asymptotic tracking control of dynamic reference over homomorphically encrypted data with finite modulus—0
A method of using RSVD in residual calculation of LowBit GEMM—0
Heterogeneous quantization regularizes spiking neural network activity—0
Fronthaul-Constrained Distributed Radar Sensing—0
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior ModelsCode0
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling—0
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores—0
Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCsCode0
P4Q: Learning to Prompt for Quantization in Visual-language Models—0
Reinforcement Learning for Finite Space Mean-Field Type Games—0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
Accumulator-Aware Post-Training Quantization—0
LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ—0
Using Random Codebooks for Audio Neural AutoEncoders—0
PTQ4RIS: Post-Training Quantization for Referring Image SegmentationCode0
AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned QuantizationCode0
A Formalization of Image Vectorization by Region Merging—0
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA—0
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique—0
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization—0
Disentanglement with Factor Quantized Variational AutoencodersCode0
Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity CluesCode0
SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms—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