Analyzing Machine Learning Workloads Using a Detailed GPU Simulator

Lew, Jonathan; Shah, Deval; Pati, Suchita; Cattell, Shaylin; Zhang, Mengchi; Sandhupatla, Amruth; Ng, Curtise; Goli, Negar; Sinclair, Matthew; Rogers, Timothy G.; Aamodt, Tor M.

doi:10.1109/ispass.2019.00028

Cited by 51 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both ML and DL come under the big umbrella of artificial intelligence (AI) and aim at learning useful information from the big data 7 . These techniques have gained enormous popularity in the field of network security, over the last decade due to the invention of very powerful graphics processor units (GPUs) 8 . Both ML and DL are powerful tools in learning useful features from the network traffic and predicting the normal and abnormal activities based on the learned patterns.…”

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

606

215

View full text Add to dashboard Cite

The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

show abstract

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

606

215

View full text Add to dashboard Cite

show abstract

“…Therefore, specific microarchitectures should be used for a particular study. In this study, we target Maxwell and Pascal microarchitectures for two rea sons: (1) because they share the same ISA [24]; and (2) they have been intensively used to accelerate deep learning models [3], [12], [25].…”

Section: Tested Gpu Architecturesmentioning

confidence: 99%

Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level

Ibrahim

Liu

Yang

et al. 2020

Wseas Transactions on Systems and Control

View full text Add to dashboard Cite

Deep Neural Networks (DNNs) used in safetycritical systems cannot compromise their performance due to reliability issues. In particular, soft errors are the worst. Selective softwarebased protection solutions are among the best techniques to improve the reliability of DNNs efficiently. However, their most significant challenge is precisely hardening portions of the DNN model to avoid performance degradation. In this work, we propose a comprehensive methodology to analyze the reliability of object detection and classification algorithms run on GPUs from the lowest (instruction) evaluation level. The ultimate goal is to avoid the performance penalty of full instruction duplication by confidently identifying the vulnerable instructions. For this purpose, we propose a technique, Instruction Vulnerability Factor (IVF). By applying our methodology on ResNet and YOLO models, we demonstrate that both models’ most vulnerable instructions can be precisely determined. Moreover, we show that YOLO is more sensitive to the changes caused by soft errors than ResNet. Also, ResNet depends on the input image in its reliability, while YOLO tends to be independent.

show abstract

“…It would also be good to observe how the proposed technique translates to embedded GPUs for the for inference procedure. Lew et al [33] modify GPGPU-Sim in order to study ML workloads and analyze their behavior (e.g. run applications that use cuDNN and PyTorch).…”

Section: Related Workmentioning

confidence: 99%

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Radu

Kaszyk

Wen

et al. 2019

2019 IEEE International Symposium on Workload Characterization (IISWC)

View full text Add to dashboard Cite

Convolutional Neural Networks (CNN) are becoming a common presence in many applications and services, due to their superior recognition accuracy. They are increasingly being used on mobile devices, many times just by porting large models designed for server space, although several model compression techniques have been considered. One model compression technique intended to reduce computations is channel pruning. Mobile and embedded systems now have GPUs which are ideal for the parallel computations of neural networks and for their lower energy cost per operation. Specialized libraries perform these neural network computations through highly optimized routines. As we find in our experiments, these libraries are optimized for the most common network shapes, making uninstructed channel pruning inefficient. We evaluate higher level libraries, which analyze the input characteristics of a convolutional layer, based on which they produce optimized OpenCL (Arm Compute Library and TVM) and CUDA (cuDNN) code. However, in reality, these characteristics and subsequent choices intended for optimization can have the opposite effect. We show that a reduction in the number of convolutional channels, pruning 12% of the initial size, is in some cases detrimental to performance, leading to 2× slowdown. On the other hand, we also find examples where performance-aware pruning achieves the intended results, with performance speedups of 3× with cuDNN and above 10× with Arm Compute Library and TVM. Our findings expose the need for hardware-instructed neural network pruning.

show abstract

Analyzing Machine Learning Workloads Using a Detailed GPU Simulator

Cited by 51 publications

References 25 publications

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Analyzing the Impact of Soft Errors in Deep Neural Networks on GPUsfrom Instruction Level

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Contact Info

Product

Resources

About