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 3901–3925 of 4925 papers

TitleStatusHype
Bit Error Robustness for Energy-Efficient DNN AcceleratorsCode0
Lattice Representation Learning—0
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based BinningCode0
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression—0
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization—0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19—0
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach—0
Federated Learning With Quantized Global Model Updates—0
Some useful approximations for calculation of directivities of multibeam power patterns of large planar arrays—0
Universally Quantized Neural Compression—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
CNN Acceleration by Low-rank Approximation with Quantized Factors—0
Neural gradients are near-lognormal: improved quantized and sparse training—0
HyperFlow: Representing 3D Objects as SurfacesCode0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
O(1) Communication for Distributed SGD through Two-Level Gradient Averaging—0
Noisy One-bit Compressed Sensing with Side-Information—0
Knowledge Distillation: A Survey—0
Neural Network Activation Quantization with Bitwise Information BottlenecksCode0
Generative Design of Hardware-aware DNNs—0
An Overview of Neural Network Compression—0
Exploring the Potential of Low-bit Training of Convolutional Neural Networks—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