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 2601–2625 of 4925 papers

TitleStatusHype
Implementation of a framework for deploying AI inference engines in FPGAs—0
Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function—0
Intriguing Properties of Quantization at Scale—0
Stochastic Gradient Langevin Dynamics Based on Quantization with Increasing Resolution—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees—0
Reducing Communication for Split Learning by Randomized Top-k Sparsification—0
BRICS: Bi-level feature Representation of Image CollectionS—0
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics—0
Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking—0
A Transfer Learning and Explainable Solution to Detect mpox from Smartphones imagesCode0
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals—0
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing—0
2-bit Conformer quantization for automatic speech recognition—0
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time—0
PQA: Exploring the Potential of Product Quantization in DNN Hardware AccelerationCode0
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers—0
Just CHOP: Embarrassingly Simple LLM Compression—0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Adversarial Defenses via Vector Quantization—0
Differential Privacy with Random Projections and Sign Random Projections—0
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer—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