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 876–900 of 4925 papers

TitleStatusHype
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
A holistic approach to polyphonic music transcription with neural networksCode1
Data-Free Quantization Through Weight Equalization and Bias CorrectionCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
SparseDNN: Fast Sparse Deep Learning Inference on CPUsCode1
Sparse Fine-tuning for Inference Acceleration of Large Language ModelsCode1
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image TranslationCode1
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language ProcessingCode1
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
Mixed Precision DNNs: All you need is a good parametrizationCode1
Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint ReductionCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic QuantizationCode1
PM-KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMsCode1
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays—0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model—0
A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints—0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization—0
Achieving Robustness in Blind Modulo Analog-to-Digital Conversion—0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
ATHEENA: A Toolflow for Hardware Early-Exit Network Automation—0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings—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