A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms

Bavikadi, Sathwika; Dhavlle, Abhijitt; Ganguly, Amlan; Haridass, Anand; Hendy, Hagar; Merkel, Cory; Reddi, Vijay Janapa; Sutradhar, Purab Ranjan; Joseph, Arun; Dinakarrao, Sai Manoj Pudukotai

doi:10.1109/mdat.2022.3161126

Cited by 28 publications

(7 citation statements)

References 143 publications

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“…This list of references is not meant to be complete. For recent and thorough surveys on accelerator design for ANNs and SNNs, the readers are referred to [3], [4], [6]- [9] and [5], respectively.…”

Section: Ai Hardware Acceleratorsmentioning

confidence: 99%

“…From a hardware perspective, this poses severe challenges of data storage, movement, and processing speed on conventional Central Processing Units (CPUs) with a traditional Von Neumann computer architecture, commonly known as the memory wall problem [2]. To this end, there are intense and on-going efforts nowadays towards designing dedicated and customized processors for AI [3]- [9], referred to as AI hardware accelerators, which belong to the larger family of domain-specific computing paradigms. Widely used AI hardware accelerators today are Graphics Processing Unit (GPUs) and Field-Programmable Gate Arrays (FPGAs), but orders of magnitude of energy-speed improvement can be achieved with Application-Specific Integrated Circuits (ASICs).…”

Section: Introductionmentioning

confidence: 99%

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Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives

Liu

Stratigopoulos³

2023

IEEE Des. Test

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In recent years, there has been an expedited trend in embracing bold and radical innovation of computer architectures, aiming at the continuation of computing performance improvement despite the slowed-down physical device scaling. One new frontier in this field focuses on Artificial Intelligence (AI) hardware. While functionality of AI hardware still remains the main focus, testability and dependability of these new architectures need to be addressed before the mainstream adoption. This survey paper covers the state-of-the-art in research and development of dependability and testability solutions for AI hardware including digital or analog implementations of Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs), used in accelerators and neuromorphic designs. Trends, challenges and perspectives are also discussed in this paper.

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Section: Ai Hardware Acceleratorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives

Liu

Stratigopoulos³

2023

IEEE Des. Test

View full text Add to dashboard Cite

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“…As the aforementioned defense is developed based on software, researchers have started to shift the focus from software to hardware [83][84][85][86][87][88]. There have been a new research trending which comprises of leveraging neuromorphic computing to provide a robust defense against the adversaries.…”

Section: Need For Rram-neuromorphic Architecture Based Defense Agains...mentioning

confidence: 99%

Design of secure and robust cognitive system for malware detection

Shukla¹

2022

Preprint

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The computer systems for decades have been threatened by various types of hardware and software attacks of which Malware have been one of the pivotal issues. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even disrupt the entire system. There have been techniques to detect malware by performing static and dynamic analysis of malware files, but, stealthy malware has circumvented the static analysis method and for dynamic analysis, there have been previous works that propose different methods to detect malware. However, these techniques do not perform well on stealthy malware. Moreover, the rising trend and advancements in machine learning has resulted into its numerous applications in the field of computer vision, pattern recognition to providing security to hardware devices. Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes of malware based on the features extracted. Secondly, I demonstrate the effects of adversarial attacks on a reconfigurable RRAM-neuromorphic architecture with different learning algorithms and device characteristics. I also propose an integrated solution for mitigating the effects of the adversarial attack using the reconfigurable RRAM architecture.

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“…Advancements in machine learning (ML) are opening up new domains for artificial intelligence (AI) at record speed. In order to keep pace, hardware designers have also been developing new and innovative methods to accelerate and improve the efficiency of ML algorithms [1]. One of the leading ideas in this space is to closely couple computation and memory, leading to "in-memory" computing, where the memory array itself actively participates in computation [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Adversarial Attacks on Single-Layer NVM Crossbar-Based Neural Networks with Power Consumption Information

Merkel¹

2022

Preprint

Self Cite

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Adversarial attacks on state-of-the-art machine learning models pose a significant threat to the safety and security of mission-critical autonomous systems. This paper considers the additional vulnerability of machine learning models when attackers can measure the power consumption of their underlying hardware platform. In particular, we explore the utility of power consumption information for adversarial attacks on non-volatile memory crossbar-based single-layer neural networks. Our results from experiments with MNIST and CIFAR-10 datasets show that power consumption can reveal important information about the neural network's weight matrix, such as the 1-norm of its columns. That information can be used to infer the sensitivity of the network's loss with respect to different inputs. We also find that surrogate-based black box attacks that utilize crossbar power information can lead to improved attack efficiency.

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A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms

Cited by 28 publications

References 143 publications

Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives

Testability and Dependability of AI Hardware: Survey, Trends, Challenges, and Perspectives

Design of secure and robust cognitive system for malware detection

Enhancing Adversarial Attacks on Single-Layer NVM Crossbar-Based Neural Networks with Power Consumption Information

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