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 3151–3200 of 4925 papers

TitleStatusHype
Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization—0
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness—0
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and NormalizationCode2
Vis-TOP: Visual Transformer Overlay Processor—0
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays—0
Continuous Control with Action Quantization from Demonstrations—0
PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory—0
Wideband and Entropy-Aware Deep Soft Bit QuantizationCode0
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural NetworksCode1
Low-Precision Quantization for Efficient Nearest Neighbor Search—0
Graph-less Neural Networks: Teaching Old MLPs New Tricks via DistillationCode1
BNAS v2: Learning Architectures for Binary Networks with Empirical ImprovementsCode1
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and ActivationsCode1
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization—0
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language ProcessingCode1
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization—0
Toward nonlinear dynamic control over encrypted data for infinite time horizon—0
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense RetrievalCode1
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression—0
A comprehensive review of Binary Neural Network—0
Are Words the Quanta of Human Language? Extending the Domain of Quantum Cognition—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Cognitive Coding of Speech—0
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference TimeCode1
Federated Learning via Plurality VoteCode0
8-bit Optimizers via Block-wise QuantizationCode3
Shifting Capsule Networks from the Cloud to the Deep EdgeCode0
Random matrices in service of ML footprint: ternary random features with no performance lossCode1
Attention Augmented Convolutional Transformer for Tabular Time-series—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
Pre-Quantized Deep Learning Models Codified in ONNX to Enable Hardware/Software Co-Design—0
SDR: Efficient Neural Re-ranking using Succinct Document Representation—0
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing—0
Towards Efficient Post-training Quantization of Pre-trained Language Models—0
Riemannian Manifold Embeddings for Straight-Through Estimator—0
Full-Precision Free Binary Graph Neural Networks—0
Beyond Quantization: Power aware neural networks—0
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees—0
Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity—0
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization—0
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression—0
Lattice Quantization—0
Quantized sparse PCA for neural network weight compression—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
Specialized Transformers: Faster, Smaller and more Accurate NLP Models—0
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning—0
Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime—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