128 citations · 230 across the 7 of their papers we have counts for
7 papers
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon +6
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic l…
MPU: Towards Bandwidth-abundant SIMT Processor via Near-bank Computing
Xinfeng Xie, Peng Gu, Yufei Ding +3
With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wal…
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Xiaobing Chen, Yuke Wang, Xinfeng Xie +9
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce,…
SEALing Neural Network Models in Secure Deep Learning Accelerators
Pengfei Zuo, Yu Hua, Ling Liang +3
Deep learning (DL) accelerators are increasingly deployed on edge devices to support fast local inferences. However, they suffer from a new security problem, i.e., being vulnerable…
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
Xing Hu, Ling Liang, Lei Deng +7
As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that dep…
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture
Yu Ji, Youyang Zhang, Xinfeng Xie +5
Neural Network (NN) accelerators with emerging ReRAM (resistive random access memory) technologies have been investigated as one of the promising solutions to address the \textit{m…
QGAN: Quantized Generative Adversarial Networks
Peiqi Wang, Dongsheng Wang, Yu Ji +5
The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones. Despite the…