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 2351–2375 of 4925 papers

TitleStatusHype
A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays—0
Efficient multivariate sequence classification—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
Efficient Machine Translation with Model Pruning and Quantization—0
A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation—0
Adaptive Integrate-and-Fire Time Encoding Machine with Quantization—0
Efficiently Scaling Transformer Inference—0
EfficientLLM: Efficiency in Large Language Models—0
Efficient Learned Lossless JPEG Recompression—0
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime—0
A Novel Framework for Image-to-image Translation and Image Compression—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
Efficient Inferencing of Compressed Deep Neural Networks—0
CacheQuant: Comprehensively Accelerated Diffusion Models—0
A Novel Chaotic Uniform Quantizer for Speech Coding—0
Accelerated Distance Computation with Encoding Tree for High Dimensional Data—0
Discrete Audio Tokens: More Than a Survey!—0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
Efficient Inference via Universal LSH Kernel—0
Efficient Implicit Neural Compression of Point Clouds via Learnable Activation in Latent Space—0
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
A Novel Audio Representation for Music Genre Identification in MIR—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens—0
Show:102550
← PrevPage 95 of 197Next →
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