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

Showing 15761600 of 4925 papers

TitleStatusHype
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures0
Cross-Layer Optimization for Fault-Tolerant Deep Learning0
Cross-Layer Discrete Concept Discovery for Interpreting Language Models0
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems0
Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications0
A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems0
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs0
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization0
Achieving Robustness in Blind Modulo Analog-to-Digital Conversion0
CRB Analysis for Mixed-ADC Based DOA Estimation0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information0
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images0
ATHEENA: A Toolflow for Hardware Early-Exit Network Automation0
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks0
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings0
Achieving binary weight and activation for LLMs using Post-Training Quantization0
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-190
Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression0
A System-Level Solution for Low-Power Object Detection0
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part I: Stationary Signals0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified