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 2976–3000 of 4925 papers

TitleStatusHype
Task Oriented Channel State Information Quantization—0
Task-Oriented Communication Design at Scale—0
Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach—0
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI—0
Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine—0
Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need?—0
TCAQ-DM: Timestep-Channel Adaptive Quantization for Diffusion Models—0
TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs—0
TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Temporal Dynamic Quantization for Diffusion Models—0
Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization—0
Tensor Learning-based Precoder Codebooks for FD-MIMO Systems—0
Tensor Recovery from Noisy and Multi-Level Quantized Measurements—0
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT—0
TEQ: Trainable Equivalent Transformation for Quantization of LLMs—0
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs—0
Ternary and Binary Quantization for Improved Classification—0
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications—0
TernaryLLM: Ternarized Large Language Model—0
Ternary MobileNets via Per-Layer Hybrid Filter Banks—0
Ternary Neural Networks with Fine-Grained Quantization—0
Ternary Quantization: A Survey—0
Ternary Spike-based Neuromorphic Signal Processing System—0
TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction—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