research
∙
08/19/2023
GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism
Current distributed full-graph GNN training methods adopt a variant of d...
share
research
∙
10/30/2022
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Among different quantum algorithms, PQC for QML show promises on near-te...
research
∙
10/15/2022
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Variational Quantum Algorithms (VQA) are promising to demonstrate quantu...
research
∙
08/02/2022
PAN: Pulse Ansatz on NISQ Machines
Variational quantum algorithms (VQAs) have demonstrated great potentials...
research
∙
06/16/2021
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Recent works demonstrated the promise of using resistive random access m...
research
∙
05/08/2021
Kudu: An Efficient and Scalable Distributed Graph Pattern Mining Engine
This paper proposes Kudu, a general distributed execution engine with a ...
research
∙
05/04/2021
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Tensor computations overwhelm traditional general-purpose computing devi...
research
∙
12/20/2020
IntersectX: An Efficient Accelerator for Graph Mining
Graph pattern mining applications try to find all embeddings that match ...
research
∙
12/08/2020
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Deep Neural Networks (DNNs) have achieved extraordinary performance in v...
research
∙
11/06/2020
Low-Cost Floating-Point Processing in ReRAM for Scientific Computing
We propose ReFloat, a principled approach for low-cost floating-point pr...
research
∙
08/21/2020
DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition
Graph mining tasks, which focus on extracting structural information fro...
research
∙
06/30/2020
ReversiSpec: Reversible Coherence Protocol for Defending Transient Attacks
The recent works such as InvisiSpec, SafeSpec, and Cleanup-Spec, among o...
research
∙
05/17/2020
A Lightweight Isolation Mechanism for Secure Branch Predictors
Recently exposed vulnerabilities reveal the necessity to improve the sec...
research
∙
04/23/2020
PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
Deep neural network (DNN) has emerged as the most important and popular ...
research
∙
02/28/2020
A Comprehensive Evaluation of RDMA-enabled Concurrency Control Protocols
On-line transaction processing (OLTP) applications require efficient dis...
research
∙
01/01/2020
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
With the emergence of a spectrum of high-end mobile devices, many applic...
research
∙
09/17/2019
Heterogeneity-Aware Asynchronous Decentralized Training
Distributed deep learning training usually adopts All-Reduce as the sync...
research
∙
07/22/2019
A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology
The Adiabatic Quantum-Flux-Parametron (AQFP) superconducting technology ...
research
∙
07/03/2019
Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
research
∙
02/04/2019
Hop: Heterogeneity-Aware Decentralized Training
Recent work has shown that decentralized algorithms can deliver superior...
research
∙
01/07/2019
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
With the rise of artificial intelligence in recent years, Deep Neural Ne...
research
∙
12/31/2018
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
To facilitate efficient embedded and hardware implementations of deep ne...
research
∙
12/12/2018
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
research
∙
09/25/2018
An Efficient Framework for Implementing Persist Data Structures on Remote NVM
The byte-addressable Non-Volatile Memory (NVM) is a promising technology...
research
∙
02/18/2018
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Hardware accelerations of deep learning systems have been extensively in...
research
∙
02/02/2018
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Bayesian Neural Networks (BNNs) have been proposed to address the proble...
research
∙
08/29/2017
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
Large-scale deep neural networks (DNNs) are both compute and memory inte...
research
∙
08/21/2017
GraphR: Accelerating Graph Processing Using ReRAM
This paper presents GRAPHR, the first ReRAM-based graph processing accel...
research
∙
11/18/2016
