213 citations · 934 across the 47 of their papers we have counts for
47 papers
Leveraging 3D Technologies for Hardware Security: Opportunities and Challenges
Peng Gu, Shuangchen Li, Dylan Stow +4
3D die stacking and 2.5D interposer design are promising technologies to improve integration density, performance and cost. Current approaches face serious issues in dealing with e…
Thermal-Aware 3D Design for Side-Channel Information Leakage
Dylan Stow, Russell Barnes, Eren Kurshan +1
Side-channel attacks are important security challenges as they reveal sensitive information about on-chip activities. Among such attacks, the thermal side-channel has been shown to…
Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging
Zhaoyang Du, Yijin Guan, Tianchan Guan +3
Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast an…
Heuristic Adaptability to Input Dynamics for SpMM on GPUs
Guohao Dai, Guyue Huang, Shang Yang +6
Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide hi…
LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models
Nan Wu, Jiwon Lee, Yuan Xie +1
Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requi…
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Fei Sun, Minghai Qin, Tianyun Zhang +6
Deep neural networks (DNNs) have been proven to be effective in solving many real-life problems, but its high computation cost prohibits those models from being deployed to edge de…
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
Hengrui Zhang, Zhongming Yu, Guohao Dai +4
Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimiz…
Paulihedral: A Generalized Block-Wise Compiler Optimization Framework For Quantum Simulation Kernels
Gushu Li, Anbang Wu, Yunong Shi +3
The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compil…
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Ling Liang, Zheng Qu, Zhaodong Chen +6
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits thei…
Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction
Guyue Huang, Guohao Dai, Yu Wang +2
Sparse matrix-vector and matrix-matrix multiplication (SpMV and SpMM) are fundamental in both conventional (graph analytics, scientific computing) and emerging (sparse DNN, GNN) do…
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
Xiaolong Ma, Minghai Qin, Fei Sun +7
Deep neural networks (DNNs) are effective in solving many real-world problems. Larger DNN models usually exhibit better quality (e.g., accuracy) but their excessive computation res…
A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications
Eren Kurshan, Hai Li, Mingoo Seok +1
Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlig…
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
Nan Wu, Yuan Xie, Cong Hao
Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes concea…
A Survey of Machine Learning for Computer Architecture and Systems
Nan Wu, Yuan Xie
It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship…
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…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the…
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
Yang Zhao, Xiaohan Chen, Yue Wang +6
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Weitao Li, Pengfei Xu, Yang Zhao +3
Resistive-random-access-memory (ReRAM) based processing-in-memory (RPIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources…
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Weihua He, YuJie Wu, Lei Deng +6
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Sp…
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
Fei Sun, Minghai Qin, Tianyun Zhang +3
Neural network models are widely used in solving many challenging problems, such as computer vision, personalized recommendation, and natural language processing. Those models are…
Characterizing and Understanding GCNs on GPU
Mingyu Yan, Zhaodong Chen, Lei Deng +4
Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of…
Memristor Hardware-Friendly Reinforcement Learning
Nan Wu, Adrien Vincent, Dmitri Strukov +1
Recently, significant progress has been made in solving sophisticated problems among various domains by using reinforcement learning (RL), which allows machines or agents to learn…
HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu +6
In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid arch…