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 1601–1650 of 4925 papers

TitleStatusHype
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Error Analysis of CORDIC Processor with FPGA Implementation—0
Error-aware Quantization through Noise Tempering—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss—0
Approximately Invertible Neural Network for Learned Image Compression—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
Error Feedback Approach for Quantization Noise Reduction of Distributed Graph Filters—0
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs—0
eSampling: Energy Harvesting ADCs—0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization—0
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA—0
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA—0
Estimating the Completeness of Discrete Speech Units—0
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
EuclidNets: Combining hardware and architecture design for Efficient Inference and Training—0
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs—0
Blockwise Compression of Transformer-based Models without Retraining—0
Angular Quantization-based Binary Codes for Fast Similarity Search—0
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Evaluating the Practicality of Learned Image Compression—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
Evaluation of Linear Implicit Quantized State System method for analyzing mission performance of power systems—0
Evaluation of quality measures for color quantization—0
Angle Estimation of a Single Source with Massive Uniform Circular Arrays—0
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform—0
Event Retrieval in Large Video Collections with Circulant Temporal Encoding—0
Distributed Inference with Sparse and Quantized Communication—0
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization—0
Exact Bias Correction and Covariance Estimation for Stereo Vision—0
Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements—0
Federated Split BERT for Heterogeneous Text Classification—0
Dynamic Stashing Quantization for Efficient Transformer Training—0
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing—0
Dynamic Signal Measurements Based on Quantized Data—0
eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor—0
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks—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