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Found 16 papers, 2 papers with code
SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on Mobile
Focusing on emerging transformers (specifically the ones with computationally efficient Swin-like architectures) and large models (e. g., Stable Diffusion and LLMs) based on transformers, we observe that layout transformations between the computational operators cause a significant slowdown in these applications.
Efficient Large Language Models Fine-Tuning On Graphs
In this work, we introduce a novel and efficient approach for the end-to-end fine-tuning of Large Language Models (LLMs) on TAGs, named LEADING.
BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs
It fills the gap by proposing a series of abstractions and techniques to map binary GNNs and their computations best to fit the nature of bit manipulations on GPUs.
Survey: Exploiting Data Redundancy for Optimization of Deep Learning
It surveys hundreds of recent papers on the topic, introduces a novel taxonomy to put the various techniques into a single categorization framework, offers a comprehensive description of the main methods used for exploiting data redundancy in improving multiple kinds of DNNs on data, and points out a set of research opportunities for future to explore.
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years.
CoCoPIE XGen: A Full-Stack AI-Oriented Optimizing Framework
There is a growing demand for shifting the delivery of AI capability from data centers on the cloud to edge or end devices, exemplified by the fast emerging real-time AI-based apps running on smartphones, AR/VR devices, autonomous vehicles, and various IoT devices.
Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card
Autonomous driving is of great interest in both research and industry.
Coarsening Optimization for Differentiable Programming
This paper presents a novel optimization for differentiable programming named coarsening optimization.
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning.
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
3D object detection is an important task, especially in the autonomous driving application domain.
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism.
CoCoPIE: Making Mobile AI Sweet As PIE --Compression-Compilation Co-Design Goes a Long Way
Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning and inference.
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?
How can we make software analytics simpler and faster?
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults.
How to "DODGE" Complex Software Analytics?
Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i. e., automatic tools that find good settings for a learner's control parameters.
Hyperparameter Optimization for Effort Estimation
We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects.
Software Engineering
