Section: New Results

Compilation and Synthesis for Reconfigurable Platform

Superword-Level Parallelism-Aware Word Length Optimization

Participants : Steven Derrien, Ali Hassan El Moussawi.

Many embedded processors do not support floating-point arithmetic in order to comply with strict cost and power consumption constraints. But, they generally provide support for SIMD as a mean to improve performance for little cost overhead. Achieving good performance when targeting such processors requires the use of fixed-point arithmetic and efficient exploitation of SIMD data-path. To reduce time-to-market, automatic SIMDization – such as superword level parallelism (SLP) extraction – and floating-point to fixed-point conversion methodologies have been proposed. In [33], we showed that applying these transformations independently is not efficient. We proposed an SLP-aware word length optimization algorithm to jointly perform floating-point to fixed-point conversion and SLP extraction. We implemented the proposed approach in a source-to-source compiler framework and evaluated it on several embedded processors. Experimental results illustrated the validity of our approach with performance improvement by up to 40% for a limited loss in accuracy.

Automatic Parallelization Techniques for Time-Critical Systems

Participants : Steven Derrien, Imen Fassi, Thomas Lefeuvre.

Real-time systems are ubiquitous, and many of them play an important role in our daily life. In hard real-time systems, computing the correct results is not the only requirement. In addition, the results must be produced within pre-determined timing constraints, typically deadlines. To obtain strong guarantees on the system temporal behavior, designers must compute upper bounds of the Worst-Case Execution Times (WCET) of the tasks composing the system. WCET analysis is confronted with two challenges: (i) extracting knowledge of the execution flow of an application from its machine code, and (ii) modeling the temporal behavior of the target platform. Multi-core platforms make the latter issue even more challenging, as interference caused by concurrent accesses to shared resources have also to be modeled. Accurate WCET analysis is facilitated by predictable hardware architectures. For example, platforms using ScratchPad Memories (SPMs) instead of caches are considered as more predictable. However SPM management is left to the programmer-managed, making them very difficult to use, especially when combined with complex loop transformations needed to enable task level parallelization. Many researches have studied how to combine automatic SPM management with loop parallelization at the compiler level.It has been shown that impressive average-case performance improvements could be obtained on compute intensive kernels, but their ability to reduce WCET estimates remains to be demonstrated, as the transformed code does not lends itself well to WCET analysis.

In the context of the ARGO project, and in collaboration with members of the PACAP team, we have studied how parallelizing compilers techniques should be revisited in order to help WCET analysis tools. More precisely, we have demonstrated the ability of polyhedral optimization techniques to reduce WCET estimates in the case of sequential codes, with a focus on locality improvement and array contraction. We have shown on representative real-time image processing use cases that they could bring significant improvements of WCET estimates (up to 40%) provided that the WCET analysis process is guided with automatically generated flow annotations [31].

Operator-Level Approximate Computing

Participants : Benjamin Barrois, Olivier Sentieys.

Many applications are error-resilient, allowing for the introduction of approximations in the calculations, as long as a certain accuracy target is met. Traditionally, fixed-point arithmetic is used to relax accuracy, by optimizing the bit-width. This arithmetic leads to important benefits in terms of delay, power and area. Lately, several hardware approximate operators were invented, seeking the same performance benefits. However, a fair comparison between the usage of this new class of operators and classical fixed-point arithmetic with careful truncation or rounding, has never been performed. In [27], we first compare approximate and fixed-point arithmetic operators in terms of power, area and delay, as well as in terms of induced error, using many state-of-the-art metrics and by emphasizing the issue of data sizing. To perform this analysis, we developed a design exploration framework, ApxPerf, which guarantees that all operators are compared using the same operating conditions. Moreover, operators are compared in several classical real-life applications leveraging relevant metrics. In [27], we show that considering a large set of parameters, existing approximate adders and multipliers tend to be dominated by truncated or rounded fixed-point ones. For a given accuracy level and when considering the whole computation data-path, fixed-point operators are several orders of magnitude more accurate while spending less energy to execute the application. A conclusion of this study is that the entropy of careful sizing is always lower than approximate operators, since it require significantly less bits to be processed in the data-path and stored. Approximated data therefore always contain on average a greater amount of costly erroneous, useless information.

In [26] we performed a comparison between custom fixed-point (FxP) and floating-point (FlP) arithmetic, applied to bidimensional K-means clustering algorithm. First, FxP and FlP arithmetic operators are compared in terms of area, delay and energy, for different bitwidth, using the ApxPerf2.0 framework. Finally, both are compared in the context of K-means clustering. The direct comparison shows the large difference between 8-to-16-bit FxP and FlP operators, FlP adders consuming 5-$12\times$ more energy than FxP adders, and multipliers 2-$10\times$ more. However, when applied to K-means clustering algorithm, the gap between FxP and FlP tightens. Indeed, the accuracy improvements brought by FlP make the computation more accurate and lead to an accuracy equivalent to FxP with less iterations of the algorithm, proportionally reducing the global energy spent. The 8-bit version of the algorithm becomes more profitable using FlP, which is 80% more accurate with only $1.6\times$ more energy.

Dynamic Fault-Tolerant Mapping and Scheduling on Multi-core systems

Participants : Emmanuel Casseau, Petr Dobias.

Demand on multi-processor systems for high performance and low energy consumption still increases in order to satisfy our requirements to perform more and more complex computations. Moreover, the transistor size gets smaller and their operating voltage is lower, which goes hand in glove with higher susceptibility to system failure. In order to ensure system functionality, it is necessary to conceive fault-tolerant systems. One way to tackle this issue is to makes use of both the redundancy and reconfigurable computing, especially when multi-processor platforms are targeted. Actually, multi-processor platforms can be less vulnerable when one processor is faulty because other processors can take over its scheduled tasks.

In this context, we investigate how to dynamically map and schedule tasks onto homogeneous faulty processors. We developed a run-time algorithm based on the primary/backup approach which is commonly used for its minimal resources utilization and high reliability. Its principal rule is that, when a task arrives, the system creates two identical copies: the primary copy and the backup copy. Several policies have been studied and their performances have been analyzed. We are currently refining the algorithm to reduce its complexity without decreasing performance. This work is done in collaboration with Oliver Sinnen, PARC Lab., the University of Auckland.

Energy Constrained and Real-Time Scheduling and Mapping on Multicores

Participants : Olivier Sentieys, Angeliki Kritikakou, Lei Mo.

Multicore architectures are now widely used in energy-constrained real-time systems, such as energy-harvesting wireless sensor networks. To take advantage of these multicores, there is a strong need to balance system energy, performance and Quality-of-Service (QoS). The Imprecise Computation (IC) model splits a task into mandatory and optional parts allowing to tradeoff QoS. We focus on the problem of mapping, i.e. allocating and scheduling, IC-tasks to a set of processors to maximize system QoS under real-time and energy constraints, which we formulate as a Mixed Integer Linear Programming (MILP) problem. However, state-of-the-art solving techniques either demand high complexity or can only achieve feasible (suboptimal) solutions. We develop an effective decomposition-based approach in [40] to achieve an optimal solution while reducing computational complexity. It decomposes the original problem into two smaller easier-to-solve problems: a master problem for IC-tasks allocation and a slave problem for IC-tasks scheduling. We also provide comprehensive optimality analysis for the proposed method. Through the simulations, we validate and demonstrate the performance of the proposed method, resulting in an average 55% QoS improvement with regards to published techniques.

Real-Time Scheduling of Reconfigurable Battery-Powered Multi-Core Platforms

Participants : Daniel Chillet, Aymen Gammoudi.

Reconfigurable real-time embedded systems are constantly increasingly used in applications like autonomous robots or sensor networks. Since they are powered by batteries, these systems have to be energy-aware, to adapt to their environment and to satisfy real-time constraints. For energy harvesting systems, regular recharges of battery can be estimated, and by including this parameter in the operating system, it is then possible to develop strategy able to ensure the best execution of the application until the next recharge. In this context, operating system services must control the execution of tasks to meet the application constraints. Our objective concerns the proposition of a new real-time scheduling strategy that considers execution constraints such as the deadline of tasks and the energy for heterogeneous architectures. For such systems, we first addressed homogeneous architectures and extended our work for heterogeneous systems for which each task has different execution parameters. For these two architectures models, we formulated the problem as an ILP optimisation problem that can be solved by classical solvers. Assuming that the energy consumed by the communication is dependent on the distance between processors, we proposed a mapping strategy to minimise the total cost of communication between processors by placing the dependent tasks as close as possible to each other. The proposed strategy guarantees that, when a task is mapped into the system and accepted, it is then correctly executed prior to the task deadline. Finally, as on-line scheduling is targeted for this work, we proposed heuristics to solve these problems in efficient way. These heuristics are based on the previous packing strategy developed for the mono-processor architecture case.

Run-Time Management on Multicore Platforms

Participant : Angeliki Kritikakou.

In real-time mixed-critical systems, Worst-Case Execution Time analysis (WCET) is required to guarantee that timing constraints are respected —at least for high criticality tasks. However, the WCET is pessimistic compared to the real execution time, especially for multicore platforms. As WCET computation considers the worst-case scenario, it means that whenever a high criticality task accesses a shared resource in multi-core platforms, it is considered that all cores use the same resource concurrently. This pessimism in WCET computation leads to a dramatic under utilization of the platform resources, or even failing to meet the timing constraints. In order to increase resource utilization while guaranteeing real-time guarantees for high criticality tasks, previous works proposed a run-time control system to monitor and decide when the interferences from low criticality tasks cannot be further tolerated. However, in the initial approaches, the points where the controller is executed were statically predefined. We propose a dynamic run-time control in [19] which adapts its observations to on-line temporal properties, increasing further the dynamism of the approach, and mitigating the unnecessary overhead implied by existing static approaches. Our dynamic adaptive approach allows to control the ongoing execution of tasks based on run-time information, and increases further the gains in terms of resource utilization compared with static approaches.