Efficient Adaptive Hard Real-time Multi-processor Systems

Modern computing systems are based on multi-processor systems, i.e. multiple cores on the same chip. Hard real-time systems are required to perform particular tasks within certain amount of time; failure to do so characterises an unaccepted behavior. Hard real-time systems are found in safety-critical applications, e.g. airbag control software, flight control software, etc. In safety-critical applications, failure to meet the real-time constraints can have catastrophic effects. The safe and, at the same time, efficient deployment of applications, with hard real-time constraints, on multi-processors is a challenging task. Scheduling methods and Models of Computation, that provide safe deployments, require a realistic estimation of the Worst-Case Execution Time (WCET) of tasks. The simultaneous access of shared resources by parallel tasks, causes interference delays due to hardware arbitration. Interference delays can be accounted for, with the pessimistic assumption that all possible interference can happen. The resulting schedules would be exceedingly conservative, thus the benefits of multi-processor would be significantly negated. Producing less pessimistic schedules is challenging due to the inter-dependency between WCET estimation and deployment optimisation. Accurate estimation of interference delays -and thus estimation of task WCET- depends on the way an application is deployed; deployment is an optimisation problem that depends on the estimation of task WCET. Another efficiency gap, which is of consequence in several systems (e.g. airbag control), stems from the fact that rarely tasks execute with their WCET. Safe runtime adaptation based on the Actual Execution Times, can yield additional improvements in terms of latency (more responsive systems). To achieve efficiency and retain adaptability, we propose that interference analysis should be coupled with the deployment process. The proposed interference analysis method estimates the possible amount of interference, based on an architecture and an application model. As more information is provided, such as scheduling, memory mapping, etc, the per-task interference estimation becomes more accurate. Thus, the method computes interference-sensitive WCET estimations (isWCET). Based on the isWCET method, we propose a method to break the inter-dependency between WCET estimation and deployment optimisation. Initially, the isWCETs are over-approximated, by assuming worst-case interference, and a safe deployment is derived. Subsequently, the proposed method computes accurate isWCETs by spatio-temporal exclusion, i.e. excluding interferences from non-overlapping tasks that share resources (space). Based on accurate isWCETs, the deployment solution is improved to provide better latency guarantees. We also propose a distributed runtime adaptation technique, that aims to improve run-time latency. Using isWCET estimations restricts the possible adaptations, as an adaptation might increase the interference and violate the safety guarantees. The proposed technique introduces statically scheduling dependencies between tasks that prevent additional interference. At runtime, a self-timed scheduling policy that respects these dependencies, is applied, proven to be safe, and with minimal overhead. Experimental evaluation on Kalray MPPA-256 shows that our methods improve isWCET up to 36%, guaranteed latency up to 46%, runtime performance up to 42%, with a consolidated performance gain of 50%.

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

Yasir Mahmood Qureshi

The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deployed all the way from High Performance Computing (HPC) systems and cloud servers to mobile devices. These new emerging applications like video analytics, autonomous driving, natural language processing, content recommendation, bioinformatics, and genome sequencing, have different performance/energy requirements, and are deployed on a variety of different platforms. To meet the performance and Quality-of-Service (QoS)constraints, cloud servers, and HPC platforms are comprised of many-core processors. Even the processors in today's mobile and edge devices are multi-core. To optimize energy efficiency and performance, a system-level simulator is required. This simulator must be capable of simultaneously executing multi-threaded applications in a full-system environment with a complete operating system (OS), on a heterogeneous many-core system.In this thesis, I present gem5-X, a system-level simulation platform to optimize many-core heterogeneous compute and memory architectures. Gem5-X exploration and optimization methodology is also proposed to optimize both the compute and memory sub-system for multi-thread applications. Gem5-X extends gem5 with architectural extensions for the compute and memory sub-systems, including in-caching computing accelerator, clustered heterogeneous cores, in-memory computing engine, and 3D stacked High Bandwidth Memory v2 (HBM2). It also adds support enhancements to gem5, including, Virtual Machine (VM) support, profiling support in the simulator using gperf profiler, enhanced checkpointing, and file sharing between the simulated and the host system. Finally, all the extensions and enhancements are fully supported in Full System (FS)mode of gem5-X, enabling booting and running a full Linux stack on top of the simulator, allowing almost any application running in a Linux system to be executed using gem5-X.To demonstrate the capabilities of gem5-X, various compute-dominated and memory-dominated applications are used as case studies. These state-of-the-art applications include video encoding, video analytics, VMs in the cloud, Binary Neural Networks (BNNs) and Recurrent Neural Networks (RNNs) as compute-intensive workloads. In addition to compute-dominated workloads, memory-dominated applications, including genome sequence alignment based on Next Generation Sequencing (NGS) techniques, and Convolutional Neural Networks (CNNs) are presented as case studies to demonstrate the memory sub-system extensions of gem5-X. Gem5-X exploration methodology is used to optimize architectures for these applications, achieving both performance and energy benefits. All the applications and workloads used in this thesis are case studies to showcase the capabilities of gem5-X. Gem5-X is generic and can be used to optimize and explore architectures for any multi-threaded (or single-threaded) application that can run on a Linux-based OS.

EPFL2021

Energy-Aware Processing Platform Exploration for Embedded Biosignal Analysis

Ahmed Yasir Dogan

According to the World Health Organization, lifestyle-related diseases, e.g., cardiovascular diseases are the major cause of mortality worldwide. An accurate and continuous medical supervision is highly required for diagnosis and treatment of such diseases. Our traditional healthcare delivery systems, however can’t cope with consequential increasing healthcare costs and medical management needs. Personal health monitoring systems are poised to offer large-scale and cost-effective solutions to this problem. The use of wearable, miniaturized and autonomous wireless sensor nodes, featuring continuous on-node analysis of biosignals, can indeed provide ambulatory long-term and real-time monitoring required by the patients, and enables faster coordination with medical personnel. In such autonomous nodes, due to very limited available energy resources and costly wireless transmission, an ultra-low-power (ULP) on-node processing platform for advanced biosignal analysis is crucial. In this thesis, I explore ULP processing architectures for on-node biosignal analysis applications; where commonly, moderately complex arithmetic manipulations on single- or multiple- input signals are carried out. To achieve energy efficiency while providing sufficient processing capability to apply advanced biosignal analysis, in this thesis near-threshold (near-Vt h ) computing is exploited. Hence, severe performance degradation and reliability issues, occurring at deeply scaled voltages, can be avoided. In Chapter 3, I introduce a near-Vth computing single-core architecture, consisting of a ULP core, an instruction memory (IM) and a data memory (DM). The ULP core features an instruction set architecture (ISA) customized for biosignal applications. I explore that an ISA with minimal instruction set achieves considerable energy savings compared to the state-of-the-art cores, when executing biosignal applications (i.e., up to 54% compared to an established ISA). The proposed single-core architecture accomplishes high energy efficiency for most of single-input biosignal analysis applications, since it fully exploits near-Vth computing. However, the single-core architecture achieves limited voltage scaling, hence reduced energy awareness, for most of multiple-input biosignal analysis applications, where computational workload requirements are such high that the single-core architecture can’t attain these throughputs in near-Vth regime. To alleviate the performance degradation issue that prevents the single-core architecture from exploiting near-Vt h computing typically for multiple-input biosignal analysis, I propose parallel processing of biosignals on multi-core architectures. To this end, In Chapter 4, a multiple instruction, multiple data (MIMD) multi-core architecture is introduced. The MIMD architecture comprises several ULP cores, individual IMs, and a multi-bank DM shared through a lightweight interconnect between the cores and the DM. I prove that parallel processing of multiple-input biosignals leads to better energy efficiency than the sequential processing (i.e., on a single-core) for moderate and high biosignal workloads. In particular, the MIMD architecture achieves up to 62% power savings with respect to the single-core architecture for high biosignal workloads (i.e., 167 MOps/s). On the other hand, parallel processing of multiple-input biosignals can be penalized at low workloads due to high leakage power dissipation in multi-core architectures. In particular, the MIMD architecture fails against the single-core architecture in terms of energy efficiency for workloads lighter than 1.7 MOps/s. One of the major burden of power dissipation in MIMD architectures is costly multiple instruction fetch. To mitigate this issue, I propose data-level parallelism through single instruction, multiple data (SIMD) paradigm. To this end, in Chapter 4 a novel hybrid multi-core architecture, that supports SIMD and MIMD operations, is introduced. The SIMD operations, coupled with data and instruction broadcasting, enable coordinated multiple accesses to memories, hence reduced instruction fetch power. Additionally, the hybrid multi-core architecture features partial power gating of memories to achieve leakage power savings, vital at low workloads (a few 100 kOps/s). I show that SIMD processing of multiple-input biosignals leads to better energy efficiency compared to the MIMD processing. In particular, when SIMD operations are exploited, the hybrid multi-core architecture achieves up to 45.7% power saving compared to the MIMD architecture for moderate biosignal workloads. I also ascertain that partial power gating of memories is an effective technique to alleviate leakage issue in multi-core architectures. More specifically, partial power gating of the IM in the hybrid multi-core architecture leads to 38.8% power saving at low workloads. Finally, to alleviate issues with applications involving such program parts that limit SIMD execution of applications (i.e., conditional program parts), I propose to resynchronize the cores for stable lockstep code execution in case of synchronization loss. Hence, SIMD operations are exploited even for applications with conditional program parts. To this end, in Chapter 4 a lightweight software-directed hardware synchronizer is introduced. I reveal that for applications with conditional program parts, lockstep SIMD execution accomplishes up to 64% power saving with respect to the elementary SIMD execution at moderate workloads (i.e.,89 MOps/s).

EPFL2013

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Elisabetta De Giovanni, Benoît Walter Denkinger, Simone Machetti, Fabio Montagna, Miguel Peon Quiros

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multicore systems with advanced power-saving capabilities and additional heterogeneous components. In this article, we present an approach that applies optimization and parallelization techniques uncovered by modern ultralow power (ULP) platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-s excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multicore implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead electrocardiogram (ECG) signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a heartbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multicore implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020