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 4501–4550 of 4925 papers

TitleStatusHype
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates—0
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification—0
Deep Saliency Hashing—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
SYQ: Learning Symmetric Quantization For Efficient Deep Neural NetworksCode0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Convolutional Neural Networks to Enhance Coded SpeechCode0
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images—0
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Quantizing deep convolutional networks for efficient inference: A whitepaperCode0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
Parcels of Universe or why Schr\"odinger and Fourier are so relatives?—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing—0
Detecting Dead Weights and Units in Neural Networks—0
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization—0
Static Quantized Radix-2 FFT/IFFT Processor for Constraints Analysis—0
RGCNN: Regularized Graph CNN for Point Cloud SegmentationCode0
Spreading vectors for similarity searchCode0
Deep Image Compression via End-to-End LearningCode0
Modeling Realistic Degradations in Non-blind Deconvolution—0
Playing Atari with Six NeuronsCode0
A Biresolution Spectral Framework for Product Quantization—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
Two-Step Quantization for Low-Bit Neural NetworksCode0
Feature Quantization for Defending Against Distortion of Images—0
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks—0
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression—0
Retraining-Based Iterative Weight Quantization for Deep Neural Networks—0
Convolutional neural network compression for natural language processing—0
Double Quantization for Communication-Efficient Distributed Optimization—0
Scalable Methods for 8-bit Training of Neural NetworksCode0
Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region—0
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit—0
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines—0
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Mad Max: Affine Spline Insights into Deep Learning—0
PACT: Parameterized Clipping Activation for Quantized Neural NetworksCode1
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks—0
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks—0
Discrete Factorization Machines for Fast Feature-based RecommendationCode0
Quantization Mimic: Towards Very Tiny CNN for Object Detection—0
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker VerificationCode0
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks—0
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data—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