Heterogeneous MPSoC Architectures Research Articles

A Scenario-Based DVFS-Aware Hybrid Application Mapping Methodology for MPSoCs

Sound techniques for mapping soft real-time applications to resources are indispensable for meeting the application deadlines and minimizing objectives such as energy consumption, particularly on heterogeneous MPSoC architectures. For applications with input-dependent workload variations, static mappings are not able to sufficiently cope with the run-time variation, which can lead to deadline misses or unnecessary energy consumption. As a remedy, hybrid application mapping (HAM) techniques combine a design-time optimization with run-time management that adapts the mappings dynamically to the changes of the arriving input. This paper focuses on scenario-based HAM techniques. Here, the application input space is systematically clustered such that data inside the same scenario exhibit similar characteristics concerning workload when being processed under the same operating points. This static clustering of the input space into data scenarios has proven to be a good abstraction layer for simplifying the design and employment of high-quality run-time managers. However, existing state-of-the-art scenario-based HAM approaches neglect or underutilize the synergistic interplay between mapping selection and the usage of dynamic voltage/frequency scaling (DVFS) when adapting to workload variation. By combining mapping and DVFS selection, variations in the input can be either compensated by a complete re-mapping of the application, evoking a potential high reconfiguration overhead or by just changing the DVFS settings of the resources, offering a low-overhead adaptation alternative and thus significantly reducing the necessary overhead compared to DVFS-agnostic HAM. Furthermore, DVFS enables a fine-grained adaptation of a mapped application to the input data variation, e.g., by slowing down tasks with no impact on the end-to-end latency for the current input using low-frequency DVFS settings. It is shown that this combined approach can save even more energy than a pure mapping adaptation scheme, especially in the presence of data scenarios. In particular, scenario-based design operates as a catalyst for eliciting the synergies between a combined DVFS and mapping optimization and the peculiarities inside a data scenario, i.e., exploiting the commonalities inside a data scenario by perfectly tailored DVFS settings and task mapping. In this scope, this paper proposes two supplementary scenario-based DVFS-aware HAM approaches that consistently outperform existing state-of-the-art mapping approaches in terms of the number of deadline misses and energy consumption as we demonstrate in an empirical study on the basis of four different applications and three different architectures. It is also shown that these benefits still apply to target architectures with increasing mapping migration overheads, thwarting frequent mapping reconfigurations.

Abstracts of the Total Body PET conference 2018

Abstracts of the Total Body PET conference 2018

Hardware resource utilization optimization in FPGA-based Heterogeneous MPSoC architectures

Next generation FPGA circuits will allow the integration of dozens of hard and soft cores as well as dedicated accelerators in the same chip. These Heterogeneous Multiprocessor System-on-Chip (Ht-MPSoC) architectures will allow the design of very complex System-on-Chips (SoC) on a single FPGA chip and will fulfill modern application requirements, in terms of performance/energy consumption ratio. In this paper, we extend existing FPGA-based Ht-MPSoC architectures by considering sharing hardware accelerators among the cores. In these architectures, cores on the FPGA may have different resources that can be shared in different manners. To explore the large space of possible configurations of Ht-MPSoC on FPGA, designer needs a fast and accurate exploration tool. For this reason, a Mixed Integer Programming (MIP) model is also proposed to determine the Ht-MPSoC configuration that consumes the least HW resources while respecting the application execution time constraints. Using our MIP model, the design space of several hundreds of private and shared HW accelerators can be explored in a reasonable time with high accuracy.

VMODEX: A novel visualization tool for rapid analysis of heuristic-based multi-objective design space exploration of heterogeneous MPSoC architectures

System-level computer architecture simulations create large volumes of simulation data to explore alternative architectural solutions. Interpreting and drawing conclusions from this amount of simulation results can be extremely cumbersome. In other domains that also struggle with interpreting large volumes of data, such as scientific computing, data visualization is an invaluable tool. Such visualization is often domain specific and has not become widely studied and utilized for evaluating the results of computer architecture simulations. In this paper, we introduce our novel interactive visualization tool, called VMODEX, which is developed to support system-level design space exploration of MPSoC architectures. In our tool, the design space is modeled as a tree in which both the design parameters and criteria are shown in a single view. VMODEX is able to handle large design spaces and allows designers to look at the data from different perspectives and at multiple levels of abstraction. Due to the exponential design space in real problems and multiple criteria to be considered, heuristic searching algorithms are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. In VMODEX, besides the techniques provided for visualizing the DSE results, additional capabilities are developed to understand the dynamic search behavior of heuristic searching algorithms.

Energy- and reliability-aware task scheduling onto heterogeneous MPSoC architectures

Scheduling periodic tasks onto a multiprocessor architecture under several constraints such as performance, cost, energy, and reliability is a major challenge in embedded systems. In this paper, we present an Integer Linear Programming (ILP) based framework that maps a given task set onto an Heterogeneous Multiprocessor System-on-Chip (HMPSoC) architecture. Our framework can be used with several objective functions; minimizing energy consumption, minimizing cost (i.e., the number of heterogeneous processors), and maximizing reliability of the system under performance constraints. We use Dynamic Voltage Scaling (DVS) for reducing energy consumption while we employ task duplication to maximize reliability. We illustrate the effectiveness of our approach through several experiments, each with a different number of tasks to be scheduled. We also propose two heuristics based on Earliest Deadline First (EDF) algorithm for minimizing energy under performance and cost constraints. Our experiments on generated task sets show that ILP-based method reduces the energy consumption up to 62% percent against a method that does not apply DVS. Heuristic methods obtain promising results when compared to optimal results generated by our ILP-based method.