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 1876–1900 of 4925 papers

TitleStatusHype
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
Frame Quantization of Neural Networks—0
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge—0
FBQuant: FeedBack Quantization for Large Language Models—0
Frequency Autoregressive Image Generation with Continuous Tokens—0
Frequency-Biased Synergistic Design for Image Compression and Compensation—0
Frequency Disentangled Features in Neural Image Compression—0
Compact Representation for Image Classification: To Choose or to Compress?—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
FBI: Fingerprinting models with Benign Inputs—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Are disentangled representations all you need to build speaker anonymization systems?—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
From Text to Source: Results in Detecting Large Language Model-Generated Content—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks—0
Fronthaul-Constrained Distributed Radar Sensing—0
Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization—0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
FATNN: Fast and Accurate Ternary Neural Networks—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor 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