An MDE Approach for Energy Consumption Estimation in MPSoC Design

International audienceEnergy Consumption is a leading criterion to take into ac- count in the design of multiprocessor systems on chip (MP- SoC). In this paper, we present a solution to estimate the energy consumption early inMPSoC design in order to nd a good performance/energy trade-o in the design ow. This solution is based on the injection of consumption estimators between the hardware components during the co-simulation of a system at the CABA (Cycle Accurate Bit Accurate) level. These estimators are designed using a design frame- work and the corresponding SystemC code is automatically generated thanks to a model driven approach. Our solution oers an energy estimation framework without changing the IP(Intellectual Property)source codes, using standalone es- timation modules, which allows their reuse. The accuracy of this approach is checked by integrating the consumption estimation in the simulation of signicant applications

Accountable privacy preserving attribute based framework for authenticated encrypted access in clouds

In this paper, we propose an accountable privacy preserving attribute-based framework, called Ins-PAbAC, that combines attribute based encryption and attribute based signature techniques for securely sharing outsourced data contents via public cloud servers. The proposed framework presents several advantages. First, it provides an encrypted access control feature, enforced at the data owner’s side, while providing the desired expressiveness of access control policies. Second, Ins-PAbAC preserves users’ privacy, relying on an anonymous authentication mechanism, derived from a privacy preserving attribute based signature scheme that hides the users’ identifying information. Furthermore, our proposal introduces an accountable attribute based signature that enables an inspection authority to reveal the identity of the anonymously-authenticated user if needed. Third, Ins-PAbAC is provably secure, as it is resistant to both curious cloud providers and malicious users adversaries. Finally, experimental results, built upon OpenStack Swift testbed, point out the applicability of the proposed scheme in real world scenarios

An updated dashboard of complete search FSM implementations in centralized graph transaction databases

International audienc

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

Since of the advent of Industry 4.0, embedded systems have become an indispensable component of our life. However, one of the most significant disadvantages of these gadgets is their high power consumption. It was demonstrated that making efficient use of the device’s central processing unit (CPU) enhances its energy efficiency. The use of the particle swarm optimization (PSO) over an embedded environment achieves many resource problems. Difficulties of online implementation arise primarily from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, an embedded two-level PSO (E2L-PSO) for intelligent real-time simulation is introduced. This algorithm is proposed to be executed online and adapted to embedded applications. An automatic adaptation of the asynchronous embedded two-level PSO algorithm to CPU is completed. The Flexible Job Shop Scheduling Problem (FJSP) is selected to solve, due to its importance in the Industry 4.0 era. An analysis of the run-time performance on handling E2L-PSO over an STM32F407VG-Discovery card and a Raspberry Pi B+ card is conducted. By the experimental study, such optimization decreases the CPU time consumption by 10% to 70%, according to the CPU reduction needed (soft, medium, or hard reduction)

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

Since of the advent of Industry 4.0, embedded systems have become an indispensable component of our life. However, one of the most significant disadvantages of these gadgets is their high power consumption. It was demonstrated that making efficient use of the device’s central processing unit (CPU) enhances its energy efficiency. The use of the particle swarm optimization (PSO) over an embedded environment achieves many resource problems. Difficulties of online implementation arise primarily from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, an embedded two-level PSO (E2L-PSO) for intelligent real-time simulation is introduced. This algorithm is proposed to be executed online and adapted to embedded applications. An automatic adaptation of the asynchronous embedded two-level PSO algorithm to CPU is completed. The Flexible Job Shop Scheduling Problem (FJSP) is selected to solve, due to its importance in the Industry 4.0 era. An analysis of the run-time performance on handling E2L-PSO over an STM32F407VG-Discovery card and a Raspberry Pi B+ card is conducted. By the experimental study, such optimization decreases the CPU time consumption by 10% to 70%, according to the CPU reduction needed (soft, medium, or hard reduction)