Search Results for author: Sitao Huang

Found 12 papers, 6 papers with code

MicroNAS: Zero-Shot Neural Architecture Search for MCUs

no code implementations17 Jan 2024 Ye Qiao, Haocheng Xu, Yifan Zhang, Sitao Huang

Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization.

Edge-computing Neural Architecture Search

CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

no code implementations27 Jun 2023 Yifan Zhang, Arnav Vaibhav Malawade, Xiaofang Zhang, Yuhui Li, DongHwan Seong, Mohammad Abdullah Al Faruque, Sitao Huang

Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots.

Autonomous Vehicles Sensor Fusion

DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification

1 code implementation11 Apr 2023 Junyao Wang, Sitao Huang, Mohsen Imani

Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices.

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks

no code implementations16 Jan 2023 Kun Wu, Mert Hidayetoğlu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-mei Hwu

Relational graph neural networks (RGNNs) are graph neural networks (GNNs) with dedicated structures for modeling the different types of nodes and/or edges in heterogeneous graphs.

Compilation and Optimizations for Efficient Machine Learning on Embedded Systems

no code implementations6 Jun 2022 Xiaofan Zhang, Yao Chen, Cong Hao, Sitao Huang, Yuhong Li, Deming Chen

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc.

BIG-bench Machine Learning

Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

1 code implementation4 Mar 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.

Recommendation Systems

Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search

1 code implementation2 Mar 2021 Kartik Hegde, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, Christopher W. Fletcher

The key idea is to derive a smooth, differentiable approximation to the otherwise non-smooth, non-convex search space.

PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

1 code implementation20 Jan 2021 Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, JinJun Xiong, Eiman Ebrahimi, Deming Chen, Wen-mei Hwu

While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

2 code implementations9 Apr 2019 Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, JinJun Xiong, Kyle Rupnow, Wen-mei Hwu, Deming Chen

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.

C++ code object-detection +1

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