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FPGAs have found increasing adoption in data center applications since a new generation of high-level tools have become available which noticeably reduce development time for FPGA accelerators and still provide high quality of results.…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-06-15 Marius Meyer , Tobias Kenter , Christian Plessl

Deep learning compiler frameworks are gaining ground as a more portable back-end for deep learning applications on increasingly diverse hardware. However, they face the daunting challenge of matching performance offered by hand-tuned…

Machine Learning · Computer Science 2021-02-10 Jaehun Ryu , Hyojin Sung

Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable…

Emerging Technologies · Computer Science 2024-04-26 Sahil Hassan , Michael Inouye , Miguel C. Gonzalez , Ilkin Aliyev , Joshua Mack , Maisha Hafiz , Ali Akoglu

Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate accuracy losses, due to circuit and device non-idealities, Hardware-Aware…

Emerging Technologies · Computer Science 2025-01-30 Corey Lammie , Athanasios Vasilopoulos , Julian Büchel , Giacomo Camposampiero , Manuel Le Gallo , Malte Rasch , Abu Sebastian

Modern computer systems typically conbine multicore CPUs with accelerators like GPUs for inproved performance and energy efficiency. However, these sys- tems suffer from poor performance portability, code tuned for one device must be…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-05-23 Thomas L. Falch , Anne C. Elster

Future computing systems, from handhelds to supercomputers, will undoubtedly be more parallel and heterogeneous than todays systems to provide more performance and energy efficiency. Thus, GPUs are increasingly being used to accelerate…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-10-18 Saeed Taheri , Apan Qasem , Martin Burtscher

This paper presents a new approach to solve linear and nonlinear model predictive control (MPC) problems that requires small memory footprint and throughput and is particularly suitable when the model and/or controller parameters change at…

Optimization and Control · Mathematics 2021-03-25 Nilay Saraf , Alberto Bemporad

A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented…

Optimization and Control · Mathematics 2018-12-10 Tobias Englert , Andreas Völz , Felix Mesmer , Sönke Rhein , Knut Graichen

Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep Learning merits the commercial investments in these units,…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-03-02 Jens Domke , Emil Vatai , Aleksandr Drozd , Peng Chen , Yosuke Oyama , Lingqi Zhang , Shweta Salaria , Daichi Mukunoki , Artur Podobas , Mohamed Wahib , Satoshi Matsuoka

Hardware accelerators such as Graphics Processing Units (GPUs), Intel Xeon Phi co-processors (PHIs), and Field-Programmable Gate Arrays (FPGAs) are now ubiquitous in extreme-scale high performance computing (HPC), cloud, and Big data…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-08-16 Daniel Hanlon , Hamidreza Khalighzadeh , Ravi Reddy Manumachu , Alexey Lastovetsky

Large language models (LLMs) are remarked by their substantial computational requirements. To mitigate the cost, researchers develop specialized CUDA kernels, which often fuse several tensor operations to maximize the utilization of GPUs as…

Hardware Architecture · Computer Science 2025-01-15 Guoliang He , Eiko Yoneki

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency,…

Computer Vision and Pattern Recognition · Computer Science 2020-08-14 Kuan Wang , Zhijian Liu , Yujun Lin , Ji Lin , Song Han

As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hiding as many architecture-based optimization details as…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-03-17 Jacob O. Tørring , Ben van Werkhoven , Filip Petrovic , Floris-Jan Willemsen , Jirí Filipovic , Anne C. Elster

Differentiable model predictive control (MPC) offers a powerful framework for combining learning and control. However, its adoption has been limited by the inherently sequential nature of traditional optimization algorithms, which are…

Optimization and Control · Mathematics 2025-10-08 Emre Adabag , Marcus Greiff , John Subosits , Thomas Lew

Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such systems. Its high computational complexity results in high…

Systems and Control · Electrical Eng. & Systems 2024-10-28 Eivind Bøhn , Sebastien Gros , Signe Moe , Tor Arne Johansen

Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image…

Image and Video Processing · Electrical Eng. & Systems 2019-08-07 Wei-Ting Wang , Han-Lin Li , Wei-Shiang Lin , Cheng-Ming Chiang , Yi-Min Tsai

Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential…

Performance · Computer Science 2018-02-09 Peng Zhang , Jianbin Fang , Tao Tang , Canqun Yang , Zheng Wang

As exascale systems reach unprecedented concurrency, traditional performance analysis tools struggle with the overhead of massive-scale telemetry. We present an accelerated infrastructure for the hpcanalysis framework that leverages a…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-05-12 Dragana Grbic

The rapid adoption of Large Language Models (LLMs) has made GPU inference efficiency an increasingly critical system concern. The runtime of LLM workloads is largely dominated by tile-based kernels, particularly General Matrix…

Performance · Computer Science 2026-04-14 Kaixuan Zhang , Chutong Ding , Shiyou Qian , Luping Wang , Jian Cao , Guangtao Xue , Cheng Huang , Guodong Yang , Liping Zhang

We propose a simulation-based approach for performance modeling of parallel applications on high-performance computing platforms. Our approach enables full-system performance modeling: (1) the hardware platform is represented by an abstract…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-11-06 Gen Xu , Huda Ibeid , Xin Jiang , Vjekoslav Svilan , Zhaojuan Bian
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