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 4526–4550 of 4925 papers

TitleStatusHype
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