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 226–250 of 4925 papers

TitleStatusHype
PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation—0
Efficient Fine-Tuning of Quantized Models via Adaptive Rank and Bitwidth—0
Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free—0
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Efficient Vision-based Vehicle Speed Estimation—0
Aggregating empirical evidence from data strategy studies: a case on model quantization—0
Optimizing Deep Neural Networks using Safety-Guided Self CompressionCode0
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
Generative QoE Modeling: A Lightweight Approach for Telecom Networks—0
Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models—0
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Softpick: No Attention Sink, No Massive Activations with Rectified SoftmaxCode2
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech—0
TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate—0
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs—0
Partition Map-Based Fast Block Partitioning for VVC Inter CodingCode0
Pushing the boundary on Natural Language Inference—0
Fast Autoregressive Models for Continuous Latent Generation—0
Precision Neural Network Quantization via Learnable Adaptive Modules—0
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis—0
TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs—0
A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained SettingsCode0
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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