Bit-accurate energy estimation for Networks-on-Chip

Moréac, Erwan; Rossi, André; Laurent, Johann; Bomel, Pierre

doi:10.1016/j.sysarc.2017.05.002

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Moreover, authors of reference [30] proposed an Adaptive Core Mapping (ACM) algorithm for NoC systems, which dynamically responds to failed cores by replacing them with spare cores to maintain system functionality using the NOXIM simulator. Furthermore, authors of [14]…”

Section: Studies On Noximmentioning

confidence: 99%

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

et al. 2023

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Context: Network-on-Chips (NoCs) serve as essential interconnection infrastructures in Multi-processor System-on-Chip (MPSoC) designs, emphasizing flexibility, extensibility, and low power consumption. The effectiveness of communication within NoCs relies heavily on the routing algorithm employed. However, the routing process faces significant challenges, such as deadlock, livelock, congestion, and faults, which impact the Design Space Exploration (DSE) process. Objective: In this research, we propose a prediction model-based ANN with a Metaheuristic Optimization approach for predicting the utilized routing algorithm by the NoC-based MPSoC platform during the DSE in order to reduce the time required to specify the NoC-based MPSoC platform configurations. Method: The main idea of the proposed method is to develop a prediction model, specifically an Artificial Neural Network (ANN) optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN), for predicting the utilized routing algorithm in NoC-based MPSoC platform during the DSE process. The methodology consists of two phases. Firstly, an automated strategy is proposed to execute and control the NOXIM simulator, enabling the simulation and collection of NoC-based MPSoC scenarios under different constraints, rules of NoC design (including routing protocols), and performance metrics. Consequently, NoC traffic data is gathered. Secondly, building upon the work of previous researchers, who have suggested the development of hybrid models to improve accuracy, this research introduces a novel methodology incorporating an ANN optimized using the GCAOA-ANN to predict the routing algorithm employed by the NoC-based MPSoC. Results:The hybrid GCAOA-ANN model demonstrated superior performance compared to other models, including PSOGW-ANN, SMA-ANN, AOA-ANN, Random Forest, and ANN. Where the learning rate and the number of nodes in the first and second hidden layers were: (0.0023, 1, 20) for the GCAOA-ANN model. In addition, the evaluation metrics of the GCAOA-ANN model showed excellent results, with an R2 value of (99.80%), an RMSE of (0.0000135 m3/s), an MSE of (0.0000118 m3/s), an SI of (0.000077), an MBE of (0.0000028), and a MAR of (0.000187616 m3/s). Conclusion:The outcomes of this research are anticipated to offer a guide for individuals interested in conducting research on On-Chip communication routing protocols and DSE in NoC-based MPSoCs in the context of IoT using ML approaches, while the GCAOA-ANN has the potential to generalize to new problems.

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Section: Studies On Noximmentioning

confidence: 99%

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

et al. 2023

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“…We have used the Noxim-XT simulator [23] to assess the performance behavior of the multi-layer defense at the network level. This simulator is a modified version of Noxim that uses "alpha-trace" to detect successful attacks on the NoC link based on various factors.…”

Section: Network Level Evaluationsmentioning

confidence: 99%

Addressing Benign and Malicious Crosstalk in Modern System-on-Chips

Patooghy,

Elahi,

Torkaman

et al. 2023

IEEE Access

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In this study, we investigate the effects of intentional crosstalk noise on modern Multi-Processor System-on-Chips (MPSoCs). We demonstrate the potential for an adversary to inject false data into the communication network of the MPSoC, thereby creating intentional crosstalk scenarios that increase the likelihood of bit errors in other users' data. This is achieved through a pre-attack reconnaissance phase in which the attacker determines the size of the MPSoC's communication links. Based on this information, the attacker then issues false packets into the MPSoC's network to intentionally corrupt users' data. Our evaluations confirm that the attack can have serious consequences, altering the on-chip communication system of MPSoCs. Additionally, we propose a multi-layer defense mechanism that ensures the proper functioning of the MPSoC in the presence of both benign and malicious crosstalk scenarios. Our experimental results confirm the effectiveness of our proposed method in addressing at least 88% of the crosstalk situations with minimal power and performance overhead compared to alternative solutions.

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Transition Based Odd/Full Invert Coding Scheme for Crosstalk Avoidance and Low Power Consumption in NoC Links

Vinodhini

Murty

2021

Lecture Notes in Electrical Engineering

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Bit-accurate energy estimation for Networks-on-Chip

Cited by 5 publications

References 24 publications

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

Enhancing NoC-Based MPSoC Performance: A Predictive Approach With ANN and Guaranteed Convergence Arithmetic Optimization Algorithm

Addressing Benign and Malicious Crosstalk in Modern System-on-Chips

Transition Based Odd/Full Invert Coding Scheme for Crosstalk Avoidance and Low Power Consumption in NoC Links

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