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 3401–3450 of 4925 papers

TitleStatusHype
A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization—0
A^2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization—0
A 2-bit Wideband 5G mm-Wave RIS with Low Side Lobe Levels and no Quantization Lobe—0
A3 : an Analytical Low-Rank Approximation Framework for Attention—0
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps—0
DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM—0
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface—0
A 71.2-μW Speech Recognition Accelerator with Recurrent Spiking Neural Network—0
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second—0
A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM—0
Ab-initio quantum chemistry with neural-network wavefunctions—0
A Biresolution Spectral Framework for Product Quantization—0
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling—0
A Blockchain Solution for Collaborative Machine Learning over IoT—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Abstractive summarization from Audio Transcription—0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification—0
Accelerated AI Inference via Dynamic Execution Methods—0
Accelerated Distance Computation with Encoding Tree for High Dimensional Data—0
Accelerating Deep Learning Inference via Freezing—0
Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime—0
Accelerating Deep Learning with Dynamic Data Pruning—0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Accelerating Neural Network Inference by Overflow Aware Quantization—0
Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design—0
Accumulator-Aware Post-Training Quantization—0
Accuracy is Not All You Need—0
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic—0
Accurate Block Quantization in LLMs with Outliers—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search—0
Accurate INT8 Training Through Dynamic Block-Level Fallback—0
Accurate Sine-Wave Amplitude Measurements Using Nonlinearly Quantized Data—0
A Channelized Binning Method for Extraction of Dominant Color Pixel Value—0
Achieving binary weight and activation for LLMs using Post-Training Quantization—0
Achieving Robustness in Blind Modulo Analog-to-Digital Conversion—0
Differentially Quantized Gradient Methods—0
Lean classical-quantum hybrid neural network model for image classification—0
A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning—0
A CNN-based Prediction-Aware Quality Enhancement Framework for VVC—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
A Compact and Discriminative Face Track Descriptor—0
A comparative study of several parameterizations for speaker recognition—0
A comparative study of several ADPCM schemes with linear and nonlinear prediction—0
A comparison study of CNN denoisers on PRNU extraction—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