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

Showing 18011850 of 4925 papers

TitleStatusHype
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems0
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality AssessmentCode1
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization0
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion ModelsCode1
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence0
CUCL: Codebook for Unsupervised Continual LearningCode0
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time0
BinaryHPE: 3D Human Pose and Shape Estimation via BinarizationCode1
SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions0
A Blockchain Solution for Collaborative Machine Learning over IoT0
Compact 3D Gaussian Representation for Radiance FieldCode2
Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC0
Uncertainty Estimation in Multi-Agent Distributed Learning0
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming0
Eliminating Quantization Errors in Classification-Based Sound Source LocalizationCode0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs0
McQueen : Mixed Precision Quantization of Early Exit NetworksCode0
Tiny-VBF: Resource-Efficient Vision Transformer based Lightweight Beamformer for Ultrasound Single-Angle Plane Wave Imaging0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review0
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model FinetuningCode1
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing0
Compressed 3D Gaussian Splatting for Accelerated Novel View SynthesisCode0
Is Conventional SNN Really Efficient? A Perspective from Network Quantization0
A Speed Odyssey for Deployable Quantization of LLMs0
On the Impact of Calibration Data in Post-training Quantization and Pruning0
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs QuantizationCode0
Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix MultiplicationCode2
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video0
Adapting Large Language Models by Integrating Collaborative Semantics for RecommendationCode2
Data Augmentations in Deep Weight Spaces0
Generative De-Quantization for Neural Speech Codec via Latent DiffusionCode1
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome0
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation0
MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization0
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual ConnectionCode1
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling0
Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation RelaxingCode0
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained EnvironmentsCode1
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline0
Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization0
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures0
Compressed and Sparse Models for Non-Convex Decentralized Learning0
Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks0
Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINOCode4
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models0
Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified