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 2401–2425 of 4925 papers

TitleStatusHype
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference—0
Parsimonious System Identification from Fragmented Quantized Measurements—0
State Machine-based Waveforms for Channels With 1-Bit Quantization and Oversampling With Time-Instance Zero-Crossing Modulation—0
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Quantized Deep Path-following Control on a Microcontroller—0
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery—0
PowerQuant: Automorphism Search for Non-Uniform Quantization—0
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily PlantCode0
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large GenomesCode1
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
Optimized learned entropy coding parameters for practical neural-based image and video compression—0
Robust Symbol Level Precoding for Overlay Cognitive Radio Networks—0
Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image CodingCode0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
ACQ: Improving Generative Data-free Quantization Via Attention Correction—0
Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge DevicesCode0
Deep Conditional Measure Quantization—0
Masked Vector Quantization—0
RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNsCode0
Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
Security-Aware Approximate Spiking Neural Networks—0
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion—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