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Found 10 papers, 5 papers with code
SageAttention2 Technical Report: Accurate 4 Bit Attention for Plug-and-play Inference Acceleration
Second, we propose a method to smooth $Q$ and $V$, enhancing the accuracy of attention with INT4 $QK$ and FP8 $PV$.
FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling
Time series forecasting is vital in numerous web applications, influencing critical decision-making across industries.
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer.
HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction
Intraoperative hypotension (IOH) prediction using Mean Arterial Pressure (MAP) is a critical research area with significant implications for patient outcomes during surgery.
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation
Fully-supervised deep learning methods achieve promising performance with pixel-level annotations but impose a significant annotation burden on experts.
Manifold Regularization Classification Model Based On Improved Diffusion Map
Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers.
Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling
Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries.
SeesawFaceNets: sparse and robust face verification model for mobile platform
Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform.
Ranked #2 on
Lightweight Face Recognition
on AgeDB-30
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
In this paper, we are interested in boosting the representation capability of convolution neural networks which utilizing the inverted residual structure.
A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing
Chip measurements show an energy efficiency of 152/297 1b-TOPS/W and throughput of 4. 7/1. 9 1b-TOPS (scaling linearly with the matrix/input-vector element precisions) at VDD of 1. 2/0. 85V.
