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Found 8 papers, 0 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.
SoD$^2$: Statically Optimizing Dynamic Deep Neural Network
Though many compilation and runtime systems have been developed for DNNs in recent years, the focus has largely been on static DNNs.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusion
Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices.
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning
Compared with full-communication SGD, our ADPSGD achieves 1:14x to 1:27x speedups with a 100Gbps connection among computing nodes, and the speedups increase to 1:46x ~ 1:95x with a 10Gbps connection.
Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner.
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model
This paper presents techniques to detect the "offline" activity a person is engaged in when she is tweeting (such as dining, shopping or entertainment), in order to create a dynamic profile of the user, for uses such as better targeting of advertisements.
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
The large communication overhead has imposed a bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) for training deep neural networks.
