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

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Showing 3101–3150 of 4925 papers

TitleStatusHype
SUT System Description for Anti-Spoofing 2017 Challenge—0
SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention—0
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers—0
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration—0
Switchable Precision Neural Networks—0
Symbolic Models for Infinite Networks of Control Systems: A Compositional Approach—0
Symbol-Level Precoding Design for Intelligent Reflecting Surface Assisted Multi-user MIMO Systems—0
Symbol quantization in interstellar communications: methods and observations—0
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization—0
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization—0
Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition—0
SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions—0
Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems—0
T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text—0
T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition—0
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference—0
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer—0
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference—0
TaDA: Training-free recipe for Decoding with Adaptive KV Cache Compression and Mean-centering—0
TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture—0
TalkMosaic: Interactive PhotoMosaic with Multi-modal LLM Q&A Interactions—0
Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization—0
TamperNN: Efficient Tampering Detection of Deployed Neural Nets—0
Tango: rethinking quantization for graph neural network training on GPUs—0
TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
Task-Aware Quantization Network for JPEG Image Compression—0
Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels—0
Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications—0
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
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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