Search Results for author:
Found 19 papers, 4 papers with code
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
To the best of our knowledge, SparseEngine is the first deployment framework capable of performing inference of sparse models with transformer on MCUs.
DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory
Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction.
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices.
Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures
With the rapid development of artificial intelligence (AI) community, education in AI is receiving more and more attentions.
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform
In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA.
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Federated learning aims to protect users' privacy while performing data analysis from different participants.
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them.
S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks.
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient
Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications.
Mesoscale and Nanoscale Physics
Observation of Magnetic Droplets in Magnetic Tunnel Junctions
Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power.
Applied Physics
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity.
Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization
A feature decomposition and memorization (\texttt{DM}) strategy is utilized to reform the BNN inference flow in a reduced manner.
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform
Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation.
Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation
Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips.
Emerging Technologies Cryptography and Security
Spintronics based Stochastic Computing for Efficient Bayesian Inference System
Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness.
Magnetic skyrmion-based synaptic devices
Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density.
