Nano-engineered architectures for ultra-low power wireless body sensor nodes

Braojos, Rubén; Atienza, David; Aly, Mohamed M. Sabry; Wu, Tung-Yu; Wong, H.-S. Philip; Mitra, Subhasish; Ansaloni, Giovanni

doi:10.1145/2968456.2968464

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…Nonetheless, these benefits can only be leveraged by performing the DSP stage itself within a small energy envelope. In fact, thanks to progresses in the design of domain-specific analog-to-digital converters [10], [11] and wireless protocols [12], DSP tends to dominate the energy budget of smart WBSNs [13], so that any increase in its efficiency has a tangible impact at the system level.…”

Section: Introductionmentioning

confidence: 99%

HEAL-WEAR: An Ultra-Low Power Heterogeneous System for Bio-Signal Analysis

Duch

Basu

Braojos

et al. 2017

IEEE Trans. Circuits Syst. I

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Abstract-Personalized healthcare devices enable low-cost, unobtrusive and long-term acquisition of clinically-relevant biosignals. These appliances, termed Wireless Body Sensor Nodes (WBSNs), are fostering a revolution in health monitoring for patients affected by chronic ailments. Nowadays, WBSNs often embed complex digital processing routines, which must be performed within an extremely tight energy budget. Addressing this challenge, in this paper we introduce a novel computing architecture devoted to the ultra-low power analysis of biosignals. Its heterogeneous structure comprises multiple processors interfaced with a shared acceleration resource, implemented as a Coarse-Grained Reconfigurable Array (CGRA). The CGRA mesh effectively supports the execution of the intensive loops that characterize bio-signal analysis applications, while requiring a low reconfiguration overhead. Moreover, both the processors and the reconfigurable fabric feature Single-Instruction / MultipleData (SIMD) execution modes, which increase efficiency when multiple data streams are concurrently processed. The run-time behavior on the system is orchestrated by a light-weight hardware mechanism, which concurrently synchronizes processors for SIMD execution and regulates access to the reconfigurable accelerator. By jointly leveraging run-time reconfiguration and SIMD execution, the illustrated heterogeneous system achieves, when executing complex bio-signal analysis applications, speedups of up to 11.3x on the considered kernels and up to 37.2% overall energy savings, with respect to an ultra-low power multicore platform which does not feature CGRA acceleration.

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Section: Introductionmentioning

confidence: 99%

HEAL-WEAR: An Ultra-Low Power Heterogeneous System for Bio-Signal Analysis

Duch

Basu

Braojos

et al. 2017

IEEE Trans. Circuits Syst. I

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“…CPU-based 3D nanosystems can provide significant energy efficiency benefits for servers and embedded systems [1,12]. Here, we analyze domain-specific accelerator-based 3D nanosystems.…”

Section: D Nanosytemsmentioning

confidence: 99%

3D nanosystems enable embedded abundant-data computing

Hwang

Aly

Malviya

et al. 2017

Proceedings of the Twelfth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis Companion

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The world's appetite for abundant-data computing, where a massive amount of structured and unstructured data is analyzed, has increased dramatically. The computational demands of these applications, such as deep learning, far exceed the capabilities of today's systems, especially for energy-constrained embedded systems (e.g., mobile systems with limited battery capacity). These demands are unlikely to be met by isolated improvements in transistor or memory technologies, or integrated circuit (IC) architectures alone. Transformative nanosystems, which leverage the unique properties of emerging nanotechnologies to create new IC architectures, are required to deliver unprecedented functionality, performance, and energy efficiency. We show that the projected energy efficiency benefits of domain-specific 3D nanosystems is in the range of 1,000x (quantified using the product of system-level energy consumption and execution time) over today's domain-specific 2D systems with off-chip DRAM. Such a drastic improvement is key to enabling new capabilities such as deep learning in embedded systems.

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“…Further, with CMOS technology scaling to the deep sub-micron domain, the leakage power has become comparable to the logic power consumption [3]. To alleviate this problem, it is lucrative to enhance the performance of such devices and then expect them to quickly switch to the idle deep-sleep mode [4].…”

Section: Introductionmentioning

confidence: 99%

AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices

Tuli¹,

Tuli²

2020

Preprint

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Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

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Nano-engineered architectures for ultra-low power wireless body sensor nodes

Cited by 9 publications

References 34 publications

HEAL-WEAR: An Ultra-Low Power Heterogeneous System for Bio-Signal Analysis

HEAL-WEAR: An Ultra-Low Power Heterogeneous System for Bio-Signal Analysis

3D nanosystems enable embedded abundant-data computing

AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices

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