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

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Showing 1451–1500 of 4925 papers

TitleStatusHype
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval—0
Boosted Dense Retriever—0
Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques—0
EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection—0
Evaluating the Practicality of Learned Image Compression—0
Evaluation of quality measures for color quantization—0
Efficient Error-Tolerant Quantized Neural Network Accelerators—0
Efficient Evaluation of Quantization-Effects in Neural Codecs—0
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach—0
Bullion: A Column Store for Machine Learning—0
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints—0
Efficient Fine-Tuning of Quantized Models via Adaptive Rank and Bitwidth—0
Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats—0
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Event Retrieval in Large Video Collections with Circulant Temporal Encoding—0
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Efficient Inference via Universal LSH Kernel—0
Exploring FPGA designs for MX and beyond—0
Efficient Inferencing of Compressed Deep Neural Networks—0
Edge Intelligence Optimization for Large Language Model Inference with Batching and Quantization—0
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA—0
Efficient Learned Lossless JPEG Recompression—0
EfficientLLM: Efficiency in Large Language Models—0
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
EdgeFusion: On-Device Text-to-Image Generation—0
Efficient Match Kernel between Sets of Features for Visual Recognition—0
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design—0
Efficient multivariate sequence classification—0
Edge-Enabled Real-time Railway Track Segmentation—0
Efficient Neural Compression with Inference-time Decoding—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
BOMP-NAS: Bayesian Optimization Mixed Precision NAS—0
Efficient On-the-fly Category Retrieval using ConvNets and GPUs—0
Efficient Point Transformer for Large-scale 3D Scene Understanding—0
An improved wavelet-based signal-denoising architecture with less hardware consumption—0
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis—0
Can Large Language Models Understand Context?—0
Edge Deep Learning for Neural Implants—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
An Improved BKW Algorithm for LWE with Applications to Cryptography and Lattices—0
Efficient Quantum Approximate kNN Algorithm via Granular-Ball Computing—0
Adaptive Block Floating-Point for Analog Deep Learning Hardware—0
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models—0
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural 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