Evaluation of SNMP-like protocol to manage a NoC emulation platform

International audience—The Networks-on-Chip(NoCs) are currently the most appropriate communication structure for many-core embedded systems. AnFPGA-based emulation platform can drastically reduce the time needed to evaluate a NoC, even if it is composed by tens or hundreds of distributed components. These components should be timely managed in order to execute an evaluation traffic scenario. There is a lack of standard protocols to drive FPGA-based NoC emulators. Such protocols could ease the integration of emulation components developed by different designers. In this paper, we evaluate a light version of SNMP (Simple Network Management Protocol) to manage an FPGA-based NoC emulation platform. The SNMP protocol and its related components are adapted to a hardware implementation. This facilitates the configuration of the emulation nodes without FPGA-resynthesis, as well as the extraction of emulation results. Some experiments highlight that this protocol is quite simple to implement and very efficient for a light resources overhead

A Hardware Time Manager Implementation for the Xenomai Real-Time Kernel of Embedded Linux

Nowadays, the use of embedded operating systems in different embedded projects is subject to a tremendous growth. Embedded Linux is becoming one of those most popular EOSs due to its modularity, efficiency, reliability, and cost. One way to make it hard real-time is to include a real-time kernel like Xenomai. One of the key characteristics of a Real-Time Operating System (RTOS) is its ability to meet execution time deadlines deterministically. So, the more precise and flexible the time management can be, the better it can handle efficiently the determinism for different embedded applications. RTOS time precision is characterized by a specific periodic interrupt service controlled by a software time manager. The smaller the period of the interrupt, the better the precision of the RTOS, the more it overloads the CPU, and though reduces the overall efficiency of the RTOS. In this paper, we propose to drastically reduce these overheads by migrating the time management service of Xenomai into a configurable hardware component to relieve the CPU. The hardware component is implemented in a Field Programmable Gate Array coupled to the CPU. This work was achieved in a Master degree project where students could apprehend many fields of embedded systems: RTOS programming, hardware design, performance evaluation, etc.Comment: Embed With Linux (EWiLi) workshop, Lorient : France (2012

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Self-Partial and Dynamic Reconfiguration Implementation for AES using FPGA

This paper addresses efficient hardware/software implementation approaches for the AES (Advanced Encryption Standard) algorithm and describes the design and performance testing algorithm for embedded system. Also, with the spread of reconfigurable hardware such as FPGAs (Field Programmable Gate Array) embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not so safe. From another side, the self-reconfiguring platform is reported that enables an FPGA to dynamically reconfigure itself under the control of an embedded microprocessor. Hardware acceleration significantly increases the performance of embedded systems built on programmable logic. Allowing a FPGA-based MicroBlaze processor to self-select the coprocessors uses can help reduce area requirements and increase a system's versatility. The architecture proposed in this paper is an optimal hardware implementation algorithm and takes dynamic partially reconfigurable of FPGA. This implementation is good solution to preserve confidentiality and accessibility to the information in the numeric communication

A FPGA-Based Reconfigurable Software Architecture for Highly Dependable Systems

Nowadays, systems-on-chip are commonly equipped with reconfigurable hardware. The use of hybrid architectures based on a mixture of general purpose processors and reconfigurable components has gained importance across the scientific community allowing a significant improvement of computational performance. Along with the demand for performance, the great sensitivity of reconfigurable hardware devices to physical defects lead to the request of highly dependable and fault tolerant systems. This paper proposes an FPGA-based reconfigurable software architecture able to abstract the underlying hardware platform giving an homogeneous view of it. The abstraction mechanism is used to implement fault tolerance mechanisms with a minimum impact on the system performanc

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201