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 1901–1925 of 4925 papers

TitleStatusHype
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks—0
Towards Real-Time Neural Volumetric Rendering on Mobile Devices: A Measurement Study—0
Received Power Maximization Using Nonuniform Discrete Phase Shifts for RISs With a Limited Phase Range—0
HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization—0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE—0
xCOMET-lite: Bridging the Gap Between Efficiency and Quality in Learned MT Evaluation MetricsCode0
Q-SNNs: Quantized Spiking Neural Networks—0
High-Fidelity Facial Albedo Estimation via Texture Quantization—0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
Attention-aware Post-training Quantization without Backpropagation—0
MSE Minimization in RIS-Aided MU-MIMO with Discrete Phase Shifts and Fronthaul Quantization—0
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization—0
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul—0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA—0
Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization—0
An Analysis on Quantizing Diffusion Transformers—0
Optimization of Armv9 architecture general large language model inference performance based on Llama.cppCode0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—0
Memory Faults in Activation-sparse Quantized Deep Neural Networks: Analysis and Mitigation using Sharpness-aware Training—0
GEB-1.3B: Open Lightweight Large Language Model—0
Precipitation Nowcasting Using Physics Informed Discriminator Generative Models—0
Optimizing Byte-level Representation for End-to-end ASR—0
One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model—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