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 1901–1925 of 4925 papers

TitleStatusHype
FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs—0
Full-Precision Free Binary Graph Neural Networks—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Fully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Homomorphic Encryption-Enabled Distance-Based Distributed Formation Control with Distance Mismatch Estimators—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
Functional Invariants to Watermark Large Transformers—0
Functional quantization of rough volatility and applications to volatility derivatives—0
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach—0
Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective—0
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering—0
Fuzzy Norm-Explicit Product Quantization for Recommender Systems—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
FATNN: Fast and Accurate Ternary Neural Networks—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks—0
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs—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