System-Level Design of Energy-Proportional Many-Core Servers for Exascale Computing

Continuous advances in manufacturing technologies are enabling the development of more powerful and compact high-performance computing (HPC) servers made of many-core processing architectures. However, this soaring demand for computing power in the last years has grown faster than emiconductor technology evolution can sustain, and has produced as collateral undesirable effect a surge in power consumption and heat density in these new HPC servers, which result on significant performance degradation. In this keynote, I advocate to completely revise the current HPC server architectures. In particular, inspired by the mammalian brain, I propose to design a disruptive three-dimensional (3D) computing server architecture that overcomes the prevailing worst-case power and cooling provisioning paradigm for servers. This new 3D server design champions a new system-level thermal modeling, which can be used by novel proactive energy controllers for detailed heat and energy management in many-core HPC servers, thanks to micro-scale liquid cooling. Then, I will show the impact of new near-threshold computing architectures on server design, and how we can integrate new on-chip microfluidic fuel cell networks to enable energy-scalability in future generations of many-core HPC servers targeting Exascale computing.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

Edge AI Architectures for a Privacy-Preserving IoT Era

Charla organizada en la Sala de Grados A, ETSI Informática.The Internet of Things (IoT) has been hailed as the next frontier of innovation where our everyday objects are connected in ways that improve our lives and transform industries, in particular healthcare. In this talk, Prof. Atienza will first discuss the challenges of ultra-low power (ULP) Multi-Processor System-on-Chip (MPSoC) design and communication in edge Artificial Intelligence (AI) nodes for the design of smart devices and wearables in the IoT context. Then, the opportunities for edge AI architectures to conceive the next generation of federated learning systems in healthcare, as challenging use case, will be highlighted as a scalable way to deliver the IoT concept in a privacy-preserving way. This new trend of edge AI-based MPSoC architectures will need to combine new ULP heterogeneous embedded systems, including reconfigurable neural network accelerators, as well as enabling energy-scalable software layers. The final goal is to have edge AI systems that can gracefully adapt the energy consumption and precision of the IoT application outputs according to the quality requirements of our surrounding world. Moreover, they need to be able to personalize their AI algorithms by enabling training on the edge, as living organisms do to operate efficiently in the real world.Departamento de Arquitectura de Computadore

Neural Network based On-Chip Thermal Simulator

With increasing power densities, runtime thermal management is becoming a necessity in today’s systems, especially so for highly integrated Multi-Processor Systems-on-Chip (MPSoCs). In this paper, we propose a neural network (NN) based approach to implement an on-chip thermal simulator to aid such runtime management for MPSoCs. The proposed method combines the advantage of approximating the thermal properties of the chip as a linear system with the ease of fully parallel analog implementation of NNs. We perform a case study with the Niagara UltraSPARC T1 MPSoC for real-life applications, benchmarking our results with an accurate higher order Runge-Kutta (RK4) solver, that is employed in tools such as HotSpot. Within a few gate delays, the proposed NN design can simulate temperatures of the MPSoC 500 ms into the future - corresponding to thousands of iterations of the RK4 solver, with a maximum error of 1-2

Run-Time Adaptable On-Chip Predictive Thermal Triggers

With ever-increasing power densities, Dynamic Thermal Management (DTM) techniques have become mainstream in today’s systems. An important component of such techniques is the thermal trigger. It has been shown that predictive thermal triggers can outperform reactive ones. In this paper, we present a novel trade-off space of predictive thermal triggers, and identify run-time adaptability as a crucial parameter of interest. We identify the Neural Network (NN) simulator presented in [14] to have some key advantages over other predictive thermal triggers. We extend it to work for an arbitrary sensor layout configuration and to be run-time adaptable. We present experimental results on Niagara UltraSPARC T1 chip with real-life benchmark applications. Our results validate our proposed extension of the NN simulator. Our results also quantitatively establish the effectiveness of the proposed simulator for reducing, the otherwise unacceptably high errors, that can arise due to expected leakage current variation and design-time thermal modelling errors

Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection.

Objective. Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, the available solutions are not satisfactory either due to poor classification performance or high complexity to be executed in resource-constrained devices (e.g. wearable systems). Therefore, we hypothesize that obtaining high discriminative features is the main avenue to improve low-complexity seizure-detection algorithms.Approach. Inspired by how neurologists recognize ictal EEG data, and to tackle this problem by targeting resource-constrained wearable devices, we introduce a new interpretable and highly discriminative feature for EEG and iEEG, namely approximate zero-crossing (AZC). We obtain AZC by applying a polygonal approximation to mimic how our brain selects prominent patterns among noisy data and then using a zero-crossing count as a measure of the dominating frequency. By employing Kullback-Leiber divergence, leveraging CHB-MIT and SWEC-ETHZ iEEG datasets, we compare the AZC discriminative power against a set of 56 classical literature features (CLF). Moreover, we assess the performances of a low-complexity seizure detection method using only AZC features versus employing the CLF set.Main results. Three AZC features obtained with different approximation thresholds are among the five with the highest median discriminative power. Moreover, seizure classification based on only AZC features outperforms an equivalent CLF-based method. The former detects 102 and 194 seizures, against 99 and 161 for the latter (CHB-MIT and SWEC-ETHZ, respectively). Moreover, the AZC-based method keeps a similar false-alarm rate (i.e. an average of 2.1 and 1.0, against 2.0 and 0.5, per day).Significance. We propose a new feature and demonstrate its capability in seizure classification for both scalp and intracranial EEG. We envision the use of such a feature to improve outpatient monitoring with resource-constrained devices.This work was supported in part by the ML-Edge Swiss National Science Foundation (NSF) Research under Project (GA 20 002 0182 009/1), in part by the PEDESITE Swiss NSF Sinergia project (GA No. SCRSII5 193 813/1), in part by the European Union's Horizon 2020 research and innovation programme under the Marie Skłlodowska-Curie under Grant Agreement 754 354, and in part by the Maria Zambrano fellowship (MAZAM21/29) from the University of Basque Country and the Spanish Ministry of Universities, funded by the European Union-Next-GenerationEU

Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices

Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients’ vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%

Temperature-Aware Design and Management for 3D Multi-Core Architectures

Vertically-integrated 3D multiprocessors systems-on-chip (3D MPSoCs) provide the means to continue integrating more functionality within a unit area while enhancing manufacturing yields and runtime performance. However, 3D MPSoCs incur amplified thermal challenges that undermine the corresponding reliability. To address these issues, several advanced cooling technologies, alongside temperature-aware design-time optimizations and run-time management schemes have been proposed. In this monograph, we provide an overall survey on the recent advances in temperature-aware 3D MPSoC considerations. We explore the recent advanced cooling strategies, thermal modeling frameworks, design-time optimizations and run-time thermal management schemes that are primarily targeted for 3D MPSoCs. Our aim of proposing this survey is to provide a global perspective, highlighting the advancements and drawbacks on the recent state-of-the-ar

Self-Aware Wearable Systems in Epileptic Seizure Detection

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance

Emulation-based transient thermal modeling of 2D/3D systems-on-chip with active cooling

State-of-the-art devices in the consumer electronics market are relying more and more on Multi-Processor Systems-On-Chip (MPSoCs) as an efficient solution to meet their multiple design constrains, such as low cost, low power consumption, high performance and short time-to-market. In fact, as technology scales down, logic density and power density increase, generating hot spots that seriously affect the MPSoC performance and can physically damage the final system behavior. Moreover, forthcoming three-dimensional (3D) MPSoCs can achieve higher system integration density, but the aforementioned thermal problems are seriously aggravated. Thus, new thermal exploration tools are needed to study the temperature variation effects inside 3D MPSoCs. In this paper, we present a novel approach for fast transient thermal modeling and analysis of 3D MPSoCs with active (liquid) cooling solutions, while capturing the hardware-software interaction. In order to preserve both accuracy and speed, we propose a close-loop framework that combines the use of Field Programmable Gate Arrays (FPGAs) to emulate the hardware components of 2D/3D MPSoC platforms with a highly optimized thermal simulator, which uses an RC-based linear thermal model to analyze the liquid flow. The proposed framework offers speed-ups of more than three orders of magnitude when compared to cycle-accurate 3D MPSoC thermal simulators. Thus, this approach enables MPSoC designers to validate different hardware- and software-based 3D thermal management policies in real-time, and while running real-life applications, including liquid cooling injection contro

Free Cooling-Aware Dynamic Power Management for Green Datacenters

Free cooling, i.e., directly using outside cold air and/or water to cool down datacenters, can provide significant power savings of datacenters. However, due to the limited cooling capability, which is tightly coupled with climate conditions, free cooling is currently used only in limited locations (e.g., North Europe) and periods of the year. Moreover, the applicability of free cooling is further restricted along with the conservative assumption on workload characteristics and the virtual machine (VM) consolidation technique as they require to provision higher cooling capability. This paper presents a dynamic power management scheme, which extends the applicability of free cooling by judiciously consolidating VMs exploiting time-varying workload characteristics of datacenter as well as climate conditions, in order to minimize the power consumption of the entire datacenter while satisfying service-level agreement (SLA) requirements. Additionally, we propose the use of a receding horizon control scheme in order to prevent frequent cooling mode transitions. Experimental results show that the proposed solution provides up to 25.7% power savings compared to conventional free cooling decision schemes, which uses free cooling only when the outside temperature is lower than predefined threshold temperature