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 1601–1625 of 4925 papers

TitleStatusHype
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms—0
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning—0
E^2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
Angle Estimation of a Single Source with Massive Uniform Circular Arrays—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
DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI—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
ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector 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
Estimation and Quantization of Expected Persistence Diagrams—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
CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture—0
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—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