Ehsan Saboori (PhD, Eng)

This paper presents a fast and cycle accurate simulation environment for early power-performance analysis of multithreaded applications targeted to symmetric multiprocessing embedded architectures. Our simulation environment leverages the hybrid prototyping technique, where a lightweight emulation kernel performs logical simulation of multiple identical cores on top of a single physical instance of a core. The technique does not require a detailed timing model of the core hardware because the… Show more

This paper presents a fast and cycle accurate simulation environment for early power-performance analysis of multithreaded applications targeted to symmetric multiprocessing embedded architectures. Our simulation environment leverages the hybrid prototyping technique, where a lightweight emulation kernel performs logical simulation of multiple identical cores on top of a single physical instance of a core. The technique does not require a detailed timing model of the core hardware because the application threads execute directly on the target core. Previous work on hybrid prototyping supported modeling of only statically scheduled threads, thereby severely limiting its modeling capabilities. In this work, we describe the modeling of dynamic RTOS scheduler as well as hardware interrupts on top of the emulation kernel, in order to support the simulation of unmodified multi-threaded applications. Our experimental results demonstrate the high accuracy, simulation speed and scalability of our hybrid prototyping-based simulation models. Show less

This paper presents novel techniques of using hybrid prototyping for early power-performance analysis of MPSoC designs with multiple clock domains. The fundamental idea of hybrid prototyping is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. However, so far hybrid prototyping has been limited to homogeneous multicores running at the same clock frequency. Moreover, hybrid prototyping has not yet been… Show more

This paper presents novel techniques of using hybrid prototyping for early power-performance analysis of MPSoC designs with multiple clock domains. The fundamental idea of hybrid prototyping is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. However, so far hybrid prototyping has been limited to homogeneous multicores running at the same clock frequency. Moreover, hybrid prototyping has not yet been demonstrated for efficient design space exploration. Our work focuses on enhancing the capabilities of hybrid prototyping, such that it can be applied to realistic multi-clock MPSoC designs as well to perform early power-performance evaluation of MPSoC designs. Our experiments using industrial strength applications such as JPEG, MP3 and Packet Processing, demonstrate the high accuracy of our hybrid prototypes, and over two orders of magnitude improvement over software simulation speed. We also demonstrate that exploring over 150 design options using hybrid prototyping can be done with high reliability in the order of minutes compared to multiple days using conventional FPGA prototyping. Show less

The paper presents a novel dynamically reconfigurable active L1 cache model for hybrid prototyping, called DRAC. The hybrid prototyping technique simulates multicore system using an emulation kernel on top of a single physical instance of a core. We extend hybrid prototyping by supporting memory hierarchy modeling with DRAC. The presented cache model is a standalone cycle accurate model that is customized for multicore emulation. DRAC run-time configurability enables the embedded system… Show more

The paper presents a novel dynamically reconfigurable active L1 cache model for hybrid prototyping, called DRAC. The hybrid prototyping technique simulates multicore system using an emulation kernel on top of a single physical instance of a core. We extend hybrid prototyping by supporting memory hierarchy modeling with DRAC. The presented cache model is a standalone cycle accurate model that is customized for multicore emulation. DRAC run-time configurability enables the embedded system designer to simulate and explore different multicore design options without the need for full FPGA prototyping. Our experimental results show timing/energy estimation for different task mappings and different cache sizes of a JPEG encoder benchmark. We have observed 2.78% average error and 5.06% worst case error when DRAC is used as a standalone cache model in a single core design. We also observed 100% relative accuracy and less than 13% absolute worst case error in timing estimation when DRAC is used for hybrid prototyping of multicore designs. Show less

This paper presents a novel modeling technique for multicore embedded systems, called Hybrid Prototyping. The fundamental idea is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. The emulation kernel switches between tasks mapped to different cores and manages the logical simulation times of the individual cores. As a result, we can achieve fast and cycle-accurate simulation of symmetric multicore designs… Show more

This paper presents a novel modeling technique for multicore embedded systems, called Hybrid Prototyping. The fundamental idea is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. The emulation kernel switches between tasks mapped to different cores and manages the logical simulation times of the individual cores. As a result, we can achieve fast and cycle-accurate simulation of symmetric multicore designs, thereby overcoming the accuracy concerns of virtual prototyping and the scalability issues of physical prototyping. Our experiments with industrial multicore designs show that the simulation time with hybrid prototyping grows only linearly with the number of cores and the inter-core communication traffic, while providing 100% cycle accuracy. Show less

Dual-Path is an anonymous peer-to-peer approach which provides requester anonymity. This approach provides anonymity between a requester and a provider in peer-to-peer networks with trusted servers called suppernode so the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. Dual-Path establishes two paths for transmitting data. These paths called Request path and Response path. The first one is used for requesting… Show more

Dual-Path is an anonymous peer-to-peer approach which provides requester anonymity. This approach provides anonymity between a requester and a provider in peer-to-peer networks with trusted servers called suppernode so the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. Dual-Path establishes two paths for transmitting data. These paths called Request path and Response path. The first one is used for requesting data and the second one is used for sending the requested data to the requester. As Dual-Path approach is similar to Crowds approach, this article compares reliability and performance of Dual-Path and Crowds. For this purpose a simulator is developed and several scenarios are defined to compare Dual-Path and Crowds in different situations. In chapter 2 and 3 Dual-Path and Crowds approaches are briefly described. Chapter 4 is talking about simulator. Chapter 5 explains the scenarios for comparison of performance. Chapter 6 is about comparison of reliability and chapter 7 is conclusion. Show less

The real challenge in pattern recognition task and machine learning process is to train a discriminator using labeled data and use it to distinguish between future data as accurate as possible. However, most of the problems in the real world have numerous data, which labeling them is a cumbersome or even an impossible matter. Semi-supervised learning is one approach to overcome these types of problems. It uses only a small set of labeled with the company of huge remain and unlabeled data to… Show more

The real challenge in pattern recognition task and machine learning process is to train a discriminator using labeled data and use it to distinguish between future data as accurate as possible. However, most of the problems in the real world have numerous data, which labeling them is a cumbersome or even an impossible matter. Semi-supervised learning is one approach to overcome these types of problems. It uses only a small set of labeled with the company of huge remain and unlabeled data to train the discriminator. In semi-supervised learning, it is very essential that which data is labeled and depend on position of data it effectiveness changes. In this paper, we proposed an evolutionary approach called Artificial Immune System (AIS) to determine which data is better to be labeled to get the high quality data. The experimental results represent the effectiveness of this algorithm in finding these data points. Show less

In the most intrusion detection systems (IDS), a system tries to learn characteristics of different type of attacks by analyzing packets that sent or received in network. These packets have a lot of features. But not all of them is required to be analyzed to detect that specific type of attack. Detection speed and computational cost is another vital matter here, because in these types of problems, datasets are very huge regularly. In this paper we tried to propose a very simple and fast feature… Show more

In the most intrusion detection systems (IDS), a system tries to learn characteristics of different type of attacks by analyzing packets that sent or received in network. These packets have a lot of features. But not all of them is required to be analyzed to detect that specific type of attack. Detection speed and computational cost is another vital matter here, because in these types of problems, datasets are very huge regularly. In this paper we tried to propose a very simple and fast feature selection method to eliminate features with no helpful information on them. Result faster learning in process of redundant feature omission. We compared our proposed method with three most successful similarity based feature selection algorithm including Correlation Coefficient, Least Square Regression Error and Maximal Information Compression Index. After that we used recommended features by each of these algorithms in two popular classifiers including: Bayes and KNN classifier to measure the quality of the recommendations. Experimental result shows that although the proposed method can't outperform evaluated algorithms with high differences in accuracy, but in computational cost it has huge superiority over them. Show less

One of the most important issues in peer-to-peer networks is anonymity. The major anonymity for peer-to-peer users concerned with the users' identities and actions which can be revealed by any other members. There are many approaches proposed to provide anonymous peer-to-peer communications. An intruder can get information about the content of the data, the sender's and receiver's identities. Anonymous approaches are designed with the following three goals: to protect the identity of provider… Show more

One of the most important issues in peer-to-peer networks is anonymity. The major anonymity for peer-to-peer users concerned with the users' identities and actions which can be revealed by any other members. There are many approaches proposed to provide anonymous peer-to-peer communications. An intruder can get information about the content of the data, the sender's and receiver's identities. Anonymous approaches are designed with the following three goals: to protect the identity of provider, to protect the identity of requester and to protect the contents of transferred data between them. This article presents a new peer-to-peer approach to achieve anonymity between a requester and a provider in peer-to-peer networks with trusted servers called suppernode so that the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. This article shows that the proposed algorithm improved reliability and has more security. This algorithm, based on onion routing and randomization, protects transferring data against traffic analysis attack. The ultimate goal of this anonymous communications algorithm is to allow a requester to communicate with a provider in such a manner that nobody can determine the requester's identity and the content of transferred data. Show less

the k-means algorithm is one of the well-known and most popular clustering algorithms. K-means seeks an optimal partition of the data by minimizing the sum of squared error with an iterative optimization procedure, which belongs to the category of hill climbing algorithms. As we know hill climbing searches are famous for converging to local optimums. Since k-means can converge to a local optimum, different initial points generally lead to different convergence cancroids, which makes it… Show more

the k-means algorithm is one of the well-known and most popular clustering algorithms. K-means seeks an optimal partition of the data by minimizing the sum of squared error with an iterative optimization procedure, which belongs to the category of hill climbing algorithms. As we know hill climbing searches are famous for converging to local optimums. Since k-means can converge to a local optimum, different initial points generally lead to different convergence cancroids, which makes it important to start with a reasonable initial partition in order to achieve high quality clustering solutions. However, in theory, there exist no efficient and universal methods for determining such initial partitions. In this paper we tried to find an optimum initial partitioning for k-means algorithm. To achieve this goal we proposed a new improved version of downhill simplex search, and then we used it in order to find an optimal result for clustering approach and then compare this algorithm with Genetic Algorithm base (GA), Genetic K-Means (GKM), Improved Genetic K-Means (IGKM) and k-means algorithms. Show less

Network intrusion detection systems have become a crucial issue for computer systems security infrastructures. Different methods and algorithms are developed and proposed in recent years to improve intrusion detection systems. The most important issue in current systems is that they are poor at detecting novel anomaly attacks. These kinds of attacks refer to any action that significantly deviates from the normal behaviour which is considered intrusion. This paper proposed a model to improve… Show more

Network intrusion detection systems have become a crucial issue for computer systems security infrastructures. Different methods and algorithms are developed and proposed in recent years to improve intrusion detection systems. The most important issue in current systems is that they are poor at detecting novel anomaly attacks. These kinds of attacks refer to any action that significantly deviates from the normal behaviour which is considered intrusion. This paper proposed a model to improve this problem based on data mining techniques. Apriori algorithm is used to predict novel attacks and generate real-time rules for firewall. Apriori algorithm extracts interesting correlation relationships among large set of data items. This paper illustrates how to use Apriori algorithm in intrusion detection systems to cerate a automatic firewall rules generator to detect novel anomaly attack. Apriori is the best-known algorithm to mine association rules. This is an innovative way to find association rules on large scale. Show less

there are many approaches proposed to provide anonymous peer-to-peer communications. Data sent via peer-to-peer communications is vulnerable to traffic analysis. Traffic analysis is the process of intercepting and analysing messages in order to compromise information from patterns in communications. An intruder can get information about the content of the data, the requester's and provider's identities. Anonymous approaches are designed with the following three goals: to protect the identity of… Show more

there are many approaches proposed to provide anonymous peer-to-peer communications. Data sent via peer-to-peer communications is vulnerable to traffic analysis. Traffic analysis is the process of intercepting and analysing messages in order to compromise information from patterns in communications. An intruder can get information about the content of the data, the requester's and provider's identities. Anonymous approaches are designed with the following three goals: to protect the identity of provider, to protect the identity of requester and to protect the contents of transferred data between them. This article presents a new peer-to-peer approach to achieve anonymity between a requester and a provider in a hybrid peer-to-peer information-sharing environment with trusted servers called supper node so that the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. This article shows that the proposed approaches improved reliability and has more security. This approach, based on onion routing and randomization, protects transferring data against traffic analysis attack. The ultimate goal of this anonymous communications approach is to allow a requester to communicate with a provider in such a manner that nobody can determine the requester’s identity and the content of transferred data. Show less

this paper describes a new scheduling algorithm to distribute jobs in server farm systems. The proposed algorithm overcomes the starvation caused by SRPT (Shortest Remaining Processing Time). This algorithm is used in process scheduling in operating system approach. The algorithm was developed to be used in dispatcher scheduling. This algorithm is non-preemptive discipline, similar to SRPT, in which the priority of each job depends on its estimated run time, and also the amount of time it has… Show more

this paper describes a new scheduling algorithm to distribute jobs in server farm systems. The proposed algorithm overcomes the starvation caused by SRPT (Shortest Remaining Processing Time). This algorithm is used in process scheduling in operating system approach. The algorithm was developed to be used in dispatcher scheduling. This algorithm is non-preemptive discipline, similar to SRPT, in which the priority of each job depends on its estimated run time, and also the amount of time it has spent on waiting. Tasks in the servers are served in order of priority to optimize the system response time. The experiments show that the mean round around time is reduced in the server farm system. Show less

This paper first introduces a nonlinear robust controller for maglev systems. This controller which employs mainly sliding mode concepts ensures global stability of the closed-loop system. A theorem has been established in the paper to prove the overall system stability. To show how good the proposed controller is we apply it to the maglev system. Furthermore, it has been shown that the proposed controller is robust against plant parameter uncertainties. The Experimental result clearly approves… Show more

This paper first introduces a nonlinear robust controller for maglev systems. This controller which employs mainly sliding mode concepts ensures global stability of the closed-loop system. A theorem has been established in the paper to prove the overall system stability. To show how good the proposed controller is we apply it to the maglev system. Furthermore, it has been shown that the proposed controller is robust against plant parameter uncertainties. The Experimental result clearly approves the effectiveness of the designed controller Show less

This article proposes a new hybrid methodology, together with an associated series of experiments employing this methodology, for an evolutionary gait generator that uses trigonometric truncated Fourier series formulations with coefficients optimized by a Genetic Algorithm. The Fourier series is used to model joint angle trajectories of a simulated humanoid robot with 25 degrees of freedom. The humanoid robot in this study learns to imitate the human walking behavior on flat terrains in a… Show more

This article proposes a new hybrid methodology, together with an associated series of experiments employing this methodology, for an evolutionary gait generator that uses trigonometric truncated Fourier series formulations with coefficients optimized by a Genetic Algorithm. The Fourier series is used to model joint angle trajectories of a simulated humanoid robot with 25 degrees of freedom. The humanoid robot in this study learns to imitate the human walking behavior on flat terrains in a dynamically simulated environment. The simulation result shows the robustness of the developed walking behaviors even in extremely high and low speeds providing appropriate frequency. Number of range limitations were applied to the genetic algorithm used in this research to improve the learning period to less than 48 hours. The research seeks to improve upon the previous works on evolutionary gait generation, in robots with lower degrees of freedom. In addition, the proposed solution adapts a hybrid approach, thereby avoiding the long learning curves and unstable and slow gaits associated with evolutionary approaches. Show less