HW/SW co‐design of dedicated heterogeneous parallel systems: an extended design space exploration approach

Pomante, Luigi

doi:10.1049/iet-cdt.2013.0026

Cited by 15 publications

(15 citation statements)

References 18 publications

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“…Future work also includes adapting the proposed self-aware optimization framework to integrate dynamic profiling methods and runtime adaptive security policies. As the heterogeneous components integrated within the embedded devices within the distributed architecture will impact the overall performance, energy consumption, and security, future work includes investigating the use of automated exploration [9,10] of the heterogeneous resources within the embedded devices.…”

Section: Discussionmentioning

confidence: 99%

Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems

Nam

Lysecky

2018

Computers

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Abstract:Embedded systems continue to execute computational-and memory-intensive applications with vast data sets, dynamic workloads, and dynamic execution characteristics. Adaptive distributed and heterogeneous embedded systems are increasingly critical in supporting dynamic execution requirements. With pervasive network access within these systems, security is a critical design concern that must be considered and optimized within such dynamically adaptive systems. This paper presents a modeling and optimization framework for distributed, heterogeneous embedded systems. A dataflow-based modeling framework for adaptive streaming applications integrates models for computational latency, mixed cryptographic implementations for inter-task and intra-task communication, security levels, communication latency, and power consumption. For the security model, we present a level-based modeling of cryptographic algorithms using mixed cryptographic implementations. This level-based security model enables the development of an efficient, multi-objective genetic optimization algorithm to optimize security and energy consumption subject to current application requirements and security policy constraints. The presented methodology is evaluated using a video-based object detection and tracking application and several synthetic benchmarks representing various application types and dynamic execution characteristics. Experimental results demonstrate the benefits of a mixed cryptographic algorithm security model compared to using a single, fixed cryptographic algorithm. Results also highlight how security policy constraints can yield increased security strength and cryptographic diversity for the same energy constraint.

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Section: Discussionmentioning

confidence: 99%

Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems

Nam

Lysecky

2018

Computers

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“…In the context of Heterogeneous Multi-Processor Embedded Systems (HMPESs), this work adopts the ESL HW/SW co-design flow presented in [5][6] [7], where the most critical development step is the semi-automatic Design Space Exploration one. The main goal of this approach is to try to fully address the problem of both automatically suggest an HW/SW partitioning of the system specification and map the partitioned entities onto an automatically defined heterogeneous multi-processor architecture.…”

Section: Reference Design Space Explorationmentioning

confidence: 99%

“…Applying a WSM to the MOOP considered in this work, it is possible to define the utility function that quantifies the quality of each individual of the GA population. The cost functions (called indexes) and the methods used to evaluate them at each iteration have been defined in [5][6] [7]. In this context, the instance of an individualx is defined as a vector where the indexes represents processes and the values represents BC instances.…”

Section: Reference Design Space Explorationmentioning

confidence: 99%

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Muttillo

Giammatteo

Fiorilli

et al. 2020

Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures / 9th W

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Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. Therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. This approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about Parallel Genetic Algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. The descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded hardware;

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“…In the context of HMPESs, this work adopts the ESL HW/SW codesign flow presented in [4,16,17] where the most critical development step is the semi-automatic Design Space Exploration one. It involves several stages, from the definition of the solution space, the encoding with respect to the decision variable space, and the definition of the objective functions and the general MOOP.…”

Section: Reference Design Space Exploration Approachmentioning

confidence: 99%

Tuning DSE for Heterogeneous Multi-Processor Embedded Systems by means of a Self-Equalized Weighted Sum Method

Muttillo

Fiorilli

Mascio

2019

Proceedings of the 10th and 8th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures

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Heterogeneous multi-processor platforms are becoming widely diffused in the embedded system domain, mainly because of the opportunity to improve timing performance and, at the same time, to minimize energy/power consumption and costs. In using such kind of platforms, to be able to consider the trade-offs among different goals, a Design Space Exploration (DSE) is generally adopted. For this, existing DSE approaches typically rely on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOP) by minimizing a linear combination of weighted objective functions (i.e., Weighted Sum Method, WSM). The problem is then shifted towards the identification of weights able to represent desired tradeoffs. In such a context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an approach that, while still driven by a "decision maker", is able to self-tune weights to equalize objective functions contribution. In particular, this work presents a self-equalized WSM integrated into a genetic algorithm used to identify sub-optimal implementation alternatives in the context of an Electronic System Level HW/SW Co-Design flow. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded hardware.

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HW/SW co‐design of dedicated heterogeneous parallel systems: an extended design space exploration approach

Cited by 15 publications

References 18 publications

Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems

Mixed Cryptography Constrained Optimization for Heterogeneous, Multicore, and Distributed Embedded Systems

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Tuning DSE for Heterogeneous Multi-Processor Embedded Systems by means of a Self-Equalized Weighted Sum Method

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