A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

Braojos, Rubén; Bortolotti, Daniele; Bartolini, Andrea; Ansaloni, Giovanni; Benini, Luca; Atienza, David

doi:10.1109/tc.2016.2610426

Cited by 14 publications

(10 citation statements)

References 34 publications

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“…Many research works also design new techniques to increase the security and reliability of such systems like . Further, architecture‐level optimizations have been proposed in Wireless Body Sensor Nodes (WBSNs) that exploit emerging technologies, in order to reduce energy consumption and enhance performance …”

Section: Trends and Future Directionsmentioning

confidence: 99%

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

Tuli

Wander

et al. 2020

Internet Technology Letters

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Modern industry employs technologies for automation that may include Internet of Things (IoT), Cloud and/or Fog Computing, 5G as well as Artificial Intelligence (AI), Machine Learning (ML), or Blockchain. Currently, a part of research for the new industrial era is in the direction of improving healthcare services. This work throws light on some of the major challenges in providing affordable, efficient, secure and reliable healthcare from the viewpoint of computer and medical sciences. We describe a vision of how a holistic model can fulfill the growing demands of healthcare industry, and explain a conceptual model that can provide a complete solution for these increasing demands. In our model, we elucidate the components and their interaction at different levels, leveraging state‐of‐the art technologies in IoT, Fog computing, AI, ML and Blockchain. We finally describe current trends in this field and propose future directions to explore emerging paradigms and technologies on evolution of healthcare leveraging next generation computing systems.

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Section: Trends and Future Directionsmentioning

confidence: 99%

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

Tuli

Wander

et al. 2020

Internet Technology Letters

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“…Our strategy shares some similarities with [13], [8], [27]. As opposed to [13], our methodology does not require a strict partitioning of the computations and of the data types between exact and approximate, an approach that places a high burden on application programmers.…”

Section: Inexact and Near-threshold Computingmentioning

confidence: 99%

“…As opposed to [13], our methodology does not require a strict partitioning of the computations and of the data types between exact and approximate, an approach that places a high burden on application programmers. With respect to [8], we do not restrict the fault-prone memory area to a portion of the data memory; on the contrary, we consider (and manage) faults in the entire memory system, including the whole data memory (comprising dynamic structures such as the stacks) as well as the instruction memory. In [27] and [17], related error recovery strategies are investigated.…”

Section: Inexact and Near-threshold Computingmentioning

confidence: 99%

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

Basu

Duch

Braojos

et al. 2017

ACM Trans. Embed. Comput. Syst.

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e energy e ciency of digital architectures is tightly linked to the voltage level (Vdd) at which they operate. Aggressive voltage scaling is therefore mandatory when ultra-low power processing is required. Nonetheless, the lowest admissible Vdd is o en bounded by reliability concerns, especially since static and dynamic non-idealities are exacerbated in the near-threshold region, imposing costly guard-bands to guarantee correctness under worst-case conditions. A striking alternative, explored in this paper, waives the requirement for unconditional correctness, undergoing more relaxed constraints. First, a er a run-time failure, processing correctly resumes at a later point in time. Second, failures induce a limited ality-of-Service (QoS) degradation. We focus our investigation on the practical scenario of embedded bio-signal analysis, a domain in which energy e ciency is key, while applications are inherently error-tolerant to a certain degree. Targeting a domain-speci c multi-core platform, we present a study of the impact of inexactness on application-visible errors. en, we introduce a novel methodology to manage them, which requires minimal hardware resources and a negligible energy overhead. Experimental evidence show that, by tolerating 900 errors/hour, the resulting inexact platform can achieve an e ciency increase of up to 24%, with a QoS degradation of less than 3%. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permi ed. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org. INTRODUCTIONe emergence of embedded devices, able to continuously acquire and wirelessly transmit the sensed data, is fostering a revolution across the IT landscape [3], opening novel and exciting opportunities in many elds, ranging from environmental protection [30] to domotics [16].Among them, healthcare applications are of particular interest, especially the ones related to monitoring chronic cardiovascular disorders [1]. In this scenario, sensor appliances (named Wireless Body Sensor Nodes, WBSNs) enable the long-term acquisition of bio-signals, outside of a hospital environment and with minimal medical supervision [18].E ciency is key for WBSNs, as the saved energy translates both in smaller form factors (by requiring smaller ba eries) and longer autonomies. Herein, we focus our investigation on the energy optimization of the Digital Signal Processing (DSP) applications executing on WBSNs. Such routines analyze acquisitions, deriving compact feature sets, which are then transmi ed on the wireless link [7]. ey must be supported within a tight energy envelope, because...

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“…First, we analyze their application-level effect on two real-world bio-DSP benchmarks. Then, we introduce [7] CGRA acceleration Inexact computing [8] SIMD, multicore [9]- [11] [12]…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications

Basu

Duch

Peón-Quirós

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-In the Internet-of-Things (IoT) era, there is an increasing trend to enable intelligent behavior in edge computing sensors. Thus, a new generation of smart wearable devices for health monitoring is being developed, able to perform complex Digital Signal Processing (DSP) routines that extract features of clinical relevance from the acquired data. These new edge computing sensors for personalized healthcare must operate within a tight energy envelope; addressing the ensuing challenge, we herein introduce an inexact and heterogeneous edge computing architecture, specifically tailored to the bio-DSP domain. We observe that bio-signal analysis applications present task-level parallelism, intensive computational hotspots and a high degree of resilience towards errors. These characteristics drive our new bio-DSP edge node architecture design composed of multiple processing cores, a Coarse-Grained Reconfigurable Array (CGRA) accelerator, and hardware-software co-design support to become resilient to a non-zero probability of bit-flips at runtime. All these characteristics enable our new bio-DSP architecture to operate with an ultra-low voltage operating point. Indeed our results indicate that the energy benefits attained from the inclusion of all these characteristics in bio-DSP architectures are more than additive: task parallelism is harnessed both at the processor and the accelerator level, and the high tolerance of the CGRA towards voltage down-scaling is exploited to further decrease the IoT edge bio-DSP system energy envelope.

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A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

Cited by 14 publications

References 34 publications

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications

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