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Found 9 papers, 0 papers with code
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction
Simulating realistic interactions among traffic agents is crucial for efficiently validating the safety of autonomous driving systems.
RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference
Finally, based on the pre-built retrieval database, RAEE leverages the retrieved similar data's exiting information to guide the backbone model to exit at the layer, which is predicted by the approximated distribution.
IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion
Immunohistochemistry (IHC) plays a crucial role in pathology as it detects the over-expression of protein in tissue samples.
Pre-processing matters: A segment search method for WSI classification
Our study analyzes the impact of pre-processing parameters on inference and training across single- and multiple-domain datasets.
On the Compressibility of Quantized Large Language Models
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities.
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems
Surround Radar/Lidar can provide 360-degree view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems.
Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU
Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements.
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization
Achieving efficient execution of machine learning models has attracted significant attention recently.
Semi-Federated Scheduling of Parallel Real-Time Tasks on Multiprocessors
In this paper we propose the semi-federate scheduling approach, which only grants $x$ dedicated processors to a heavy task with processing capacity requirement $x + \epsilon$, and schedules the remaining $\epsilon$ part together with light tasks on shared processors.
Distributed, Parallel, and Cluster Computing
