211 citations · 733 across the 33 of their papers we have counts for
33 papers
GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelism
Jingji Chen, Zhuoming Chen, Xuehai Qian
Communication is a key bottleneck for distributed graph neural network (GNN) training. This paper proposes GNNPipe, a new approach that scales the distributed full-graph deep GNN t…
Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design
Zhiding Liang, Jinglei Cheng, Rui Yang +6
Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This pape…
Fidelity estimator, randomized benchmarking and ZNE for quantum pulses
Jinglei Cheng, Zhiding Liang, Rui Yang +4
Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum…
Hybrid Gate-Pulse Model for Variational Quantum Algorithms
Zhiding Liang, Zhixin Song, Jinglei Cheng +8
Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces s…
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Jinglei Cheng, Hanrui Wang, Zhiding Liang +3
Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of p…
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms
Zhiding Liang, Jinglei Cheng, Hang Ren +8
Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are it…
Variational Quantum Pulse Learning
Zhiding Liang, Hanrui Wang, Jinglei Cheng +9
Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus…
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
Wei Niu, Zhengang Li, Xiaolong Ma +6
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resou…
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Geng Yuan, Payman Behnam, Zhengang Li +8
Recent works demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector m…
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Qingcheng Xiao, Size Zheng, Bingzhe Wu +3
Tensor computations overwhelm traditional general-purpose computing devices due to the large amounts of data and operations of the computations. They call for a holistic solution c…
Kudu: An Efficient and Scalable Distributed Graph Pattern Mining Engine
Jingji Chen, Xuehai Qian
This paper proposes Kudu, a distributed execution engine with a well-defined abstraction that can be integrated with existing single-machine graph pattern mining (GPM) systems to p…
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +5
Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edg…
IntersectX: An Efficient Accelerator for Graph Mining
Gengyu Rao, Jingji Chen, Jason Yik +1
Graph pattern mining applications try to find all embeddings that match specific patterns. Compared to the traditional graph computation, graph mining applications are computation-…
ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers
Linghao Song, Fan Chen, Xuehai Qian +2
Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing req…
DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition
Jingji Chen, Xuehai Qian
Graph pattern mining (GPM) is an important application that identifies structures from graphs. Despite the recent progress, the performance gap between the state-of-the-art GPM sys…
RCP: A Low-overhead Reversible Coherence Protocol
You Wu, Xuehai Qian
This paper proposes RCP, a new reversible coherence protocol that ensures invisible speculative load execution (ISLE) with low overhead. RCP can be combined with processor mechanis…
A Lightweight Isolation Mechanism for Secure Branch Predictors
Lutan Zhao, Peinan Li, Rui Hou +5
Recently exposed vulnerabilities reveal the necessity to improve the security of branch predictors. Branch predictors record history about the execution of different programs, and…
PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
Chunhua Deng, Siyu Liao, Yi Xie +3
Deep neural network (DNN) has emerged as the most important and popular artificial intelligent (AI) technique. The growth of model size poses a key energy efficiency challenge for…
AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation
Jinglei Cheng, Haoqing Deng, Xuehai Qian
In the last decades, we have witnessed the rapid growth of Quantum Computing. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the capability of a quantum machine is lim…
Comprehensive Framework of RDMA-enabled Concurrency Control Protocols
Chao Wang, Kezhao Huang, Xuehai Qian
In this paper, we develop RCC, the first unified and comprehensive RDMA-enabled distributed transaction processing framework supporting six serializable concurrency control protoco…
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Wei Niu, Xiaolong Ma, Sheng Lin +5
With the emergence of a spectrum of high-end mobile devices, many applications that formerly required desktop-level computation capability are being transferred to these devices. H…
Heterogeneity-Aware Asynchronous Decentralized Training
Qinyi Luo, Jiaao He, Youwei Zhuo +1
Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. H…
A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology
Ruizhe Cai, Ao Ren, Olivia Chen +7
The Adiabatic Quantum-Flux-Parametron (AQFP) superconducting technology has been recently developed, which achieves the highest energy efficiency among superconducting logic famili…
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Xiaolong Ma, Sheng Lin, Shaokai Ye +10
Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or S…