105 research outputs found
Thermal modeling and analysis of advanced 3D stacked structures
AbstractThe emerging three-dimensional integrated circuits (3D ICs) offer a promising solution to mitigate the barriers of interconnect scaling in modern systems. It also provides greater design flexibility by allowing heterogeneous integration. However, 3D technology exacerbates the on-chip thermal issues and increases packaging and cooling costs. In this work, a 3D thermal model of a stacked system is developed and thermal analysis is performed in order to analyze different workload conditions using finite element simulations. The steady-state heat transfer analysis on the 3D stacked structure has been performed in order to analyze the effect of variation of die power consumption, with and without hotspots, on temperature in different layers of the stack has been analyzed. We have also investigated the effect of the interaction of hotspots has on peak temperature
Path-Based partitioning methods for 3D Networks-on-Chip with minimal adaptive routing
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Combining the benefits of 3D ICs and Networks-on-Chip (NoCs) schemes provides a significant performance gain in Chip Multiprocessors (CMPs) architectures. As multicast communication is commonly used in cache coherence protocols for CMPs and in various parallel applications, the performance of these systems can be significantly improved if multicast operations are supported at the hardware level. In this paper, we present several partitioning methods for the path-based multicast approach in 3D mesh-based NoCs, each with different levels of efficiency. In addition, we develop novel analytical models for unicast and multicast traffic to explore the efficiency of each approach. In order to distribute the unicast and multicast traffic more efficiently over the network, we propose the Minimal and Adaptive Routing (MAR) algorithm for the presented partitioning methods. The analytical and experimental results show that an advantageous method named Recursive Partitioning (RP) outperforms the other approaches. RP recursively partitions the network until all partitions contain a comparable number of switches and thus the multicast traffic is equally distributed among several subsets and the network latency is considerably decreased. The simulation results reveal that the RP method can achieve performance improvement across all workloads while performance can be further improved by utilizing the MAR algorithm. Nineteen percent average and 42 percent maximum latency reduction are obtained on SPLASH-2 and PARSEC benchmarks running on a 64-core CMP.Ebrahimi, M.; Daneshtalab, M.; Liljeberg, P.; Plosila, J.; Flich Cardo, J.; Tenhunen, H. (2014). Path-Based partitioning methods for 3D Networks-on-Chip with minimal adaptive routing. IEEE Transactions on Computers. 63(3):718-733. doi:10.1109/TC.2012.255S71873363
Concurrent Application Bias Scheduling for Energy Efficiency of Heterogeneous Multi-Core platforms
Minimizing energy consumption of concurrent applications on
heterogeneous multi-core platforms is challenging given the diversity in
energy-performance profiles of both the applications and hardware.
Adaptive learning techniques made the exhaustive Pareto-optimal space
exploration practically feasible to identify an energy-efficient
configuration. The existing approaches consider a single application's
characteristic for optimizing energy consumption. However, an optimal
configuration for a given single application may not be optimal when a
new application arrives. Although some related works do consider
concurrent applications scenarios, these approaches overlook the weight
of total energy consumption per application, restricting those from
prioritizing among applications. We address this limitation by
considering the mutual effect of concurrent applications on system-wide
energy consumption to adapt resource configuration at run-time. We
characterize each application's power-performance profile as a weighted
bias through off-line profiling. We infer this model combined with an
on-line predictive strategy to make resource allocation decisions for
minimizing energy consumption while honoring performance requirements.
The proposed strategy is implemented as a user-space process and
evaluated on a heterogeneous hardware platform of Odroid XU3 over the
Rodinia benchmark suite. Experimental results show up to 61% of energy
saving compared to the standard baseline of Linux governors and up to
27% of energy gain compared to state-of-the-art adaptive learning-based
resource management techniques.</p
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital
healthcare services to clients through remote sensing, continuous monitoring,
and data analytics. Smart eHealth applications sense input data from multiple
modalities, transmit the data to edge and/or cloud nodes, and process the data
with compute intensive machine learning (ML) algorithms. Run-time variations
with continuous stream of noisy input data, unreliable network connection,
computational requirements of ML algorithms, and choice of compute placement
among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth
applications. In this chapter, we present edge-centric techniques for optimized
compute placement, exploration of accuracy-performance trade-offs, and
cross-layered sense-compute co-optimization for ML-driven eHealth applications.
We demonstrate the practical use cases of smart eHealth applications in
everyday settings, through a sensor-edge-cloud framework for an objective pain
assessment case study
Robust PPG Peak Detection Using Dilated Convolutional Neural Networks
Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</p
Study on Glass-Epoxy-Based Low-Cost and Compact Tip-Truncated Triangular Printed Antenna
Printed antennas based on glass epoxy substrate have been developed. On the basis of required specifications and assigned frequencies, tip-truncated triangular printed antennas have been designed, analyzed, and fabricated. The performances of the antennas have been measured in terms of return loss, frequency of operation, bandwidth, and radiation pattern. Triangular microstrip antenna (TMSA) configuration consisting of copper as active radiating patch and glass epoxy as dielectric substrate has been screened out to achieve the essential characteristics and satisfying recommended low-cost antenna. The Method of Moment (MOM) analyzing techniques have been employed to realize the required specific properties, whereas optimized tip truncation technique and varying feed point location give rise to suitable LHCP or RHCP configuration of the printed antenna. The coaxial probe signal feed arrangement have been considered for this work. The proposed printed antennas are suitable for communication links between ships or buoys and
satellites specially for navigation purpose
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