353 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Hewlett Packard Enterprise (Ireland)IE11 papers
- Hewlett-Packard (United States)US10 papers
- Nanyang Technological UniversitySG6 papers
- Forschungszentrum JülichDE5 papers
- Stanford UniversityUS5 papers
- Georgia Institute of TechnologyUS4 papers
- RWTH Aachen UniversityDE4 papers
- The University of Texas at AustinUS4 papers
- University of California, BerkeleyUS4 papers
- Aalto UniversityFI3 papers
- Carnegie Mellon UniversityUS3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
51 papers
On Hardware-Aware Design and Optimization of Edge Intelligence
Shuo Huai, Hao Kong, Xiangzhong Luo +5
Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learnin…
Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning
Hao Kong, Di Liu, Xiangzhong Luo +5
In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks…
EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems
Shuo Huai, Hao Kong, Shiqing Li +5
Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge dev…
Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
Shuo Huai, Di Liu, Hao Kong +5
Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. Howe…
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Shuo Huai, Di Liu, Hao Kong +4
Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during th…
In-Network Collective Operations: Game Changer or Challenge for AI Workloads?
Torsten Hoefler, Mikhail Khalilov, Josiah Clark +9
This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this i…
Multi-Partner Project: Multi-GPU Performance Portability Analysis for CFD Simulations at Scale
Panagiotis-Eleftherios Eleftherakis, George Anagnostopoulos, Anastassis Kapetanakis +10
As heterogeneous supercomputing architectures leveraging GPUs become increasingly central to high-performance computing (HPC), it is crucial for computational fluid dynamics (CFD)…
EDAN: Towards Understanding Memory Parallelism and Latency Sensitivity in HPC
Siyuan Shen, Mikhail Khalilov, Lukas Gianinazzi +6
Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems. However, this comes at the cost of increased memory access latency d…
Modeling the Potential of Message-Free Communication via CXL.mem
Stepan Vanecek, Matthew Turner, Manisha Gajbe +2
Heterogeneous memory technologies are increasingly important instruments in addressing the memory wall in HPC systems. While most are deployed in single node setups, CXL.mem is a t…
Testing and benchmarking emerging supercomputers via the MFC flow solver
Benjamin Wilfong, Anand Radhakrishnan, Henry A. Le Berre +3
Deploying new supercomputers requires testing and evaluation via application codes. Portable, user-friendly tools enable evaluation, and the Multicomponent Flow Code (MFC), a compu…
Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization
Benjamin Wilfong, Anand Radhakrishnan, Henry Le Berre +9
We present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to…
Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures
Prabhu Vellaisamy, Thomas Labonte, Sourav Chakraborty +3
Large language model (LLM)-based inference workloads increasingly dominate data center costs and resource utilization. Therefore, understanding the inference workload characteristi…
Accelerating Hybrid XORCNF Boolean Satisfiability Problems Natively with In-Memory Computing
Haesol Im, Fabian Böhm, Giacomo Pedretti +14
The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit desig…
Hardware-level Interfaces for Hybrid Quantum-Classical Computing Systems
Konstantinos Rallis, Ioannis Liliopoulos, Evangelos Tsipas +5
The technology of Quantum Computing (QC) is continuously evolving, as researchers explore new technologies and the public gains access to quantum computers with an increasing numbe…
Hardware-Compatible Single-Shot Feasible-Space Heuristics for Solving the Quadratic Assignment Problem
Haesol Im, Chan-Woo Yang, Moslem Noori +10
Research into the development of special-purpose computing architectures designed to solve quadratic unconstrained binary optimization (QUBO) problems has flourished in recent year…
GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee, Lei, Chen +3
Shape deviation modeling and compensation in additive manufacturing are pivotal for achieving high geometric accuracy and enabling industrial-scale production. Critical challenges…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +5
We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
OpenACC offloading of the MFC compressible multiphase flow solver on AMD and NVIDIA GPUs
Benjamin Wilfong, Anand Radhakrishnan, Henry A. Le Berre +3
GPUs are the heart of the latest generations of supercomputers. We efficiently accelerate a compressible multiphase flow solver via OpenACC on NVIDIA and AMD Instinct GPUs. Optimiz…
Parallel Tempering-Inspired Distributed Binary Optimization with In-Memory Computing
Xiangyi Zhang, Fabian Böhm, Elisabetta Valiante +7
In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advant…
Portability of Fortran's `do concurrent' on GPUs
Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker +6
There is a continuing interest in using standard language constructs for accelerated computing in order to avoid (sometimes vendor-specific) external APIs. For Fortran codes, the {…
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +3
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classif…
3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder
Rachel, Chen, Wenjia Zheng +3
With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifica…