Microwatt embedded processor platform for medical system-on-chip applications

Sridhara, S.R.; DiRenzo, Michael T.; Lingam, Srinivas; Lee, Seokjun; Blazquez, R.; Maxey, Jay; Ghanem, Samer; Lee, Yu-Hung; Abdallah, Rami A.; Singh, Prashant; Goe, Manish

doi:10.1109/vlsic.2010.5560251

Cited by 61 publications

(30 citation statements)

References 13 publications

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“…UBTHRESHOLD circuits can dramatically reduce power dissipation [1], and hence, they are suitable for powerconstrained applications, such as sensor-node processors [2], [3] and medical applications [4]. Soft-error immunity of subthreshold circuits, however, has become a concern because the ultra-low voltage operation reduces the energy required to cause upsets [5], [6].…”

mentioning

confidence: 99%

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Fuketa

Harada

Hashimoto

et al. 2014

IEEE Trans. Device Mater. Relib.

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Abstract-This paper presents measurement results of alphaparticle-induced soft errors and multiple-cell upsets (MCUs) in 65-nm 10T SRAM with a wide range of supply voltage from 1.0 to 0.3 V. We reveal that the soft-error rate (SER) at 0.3 V is six times higher than that at 1.0 V and the MCU rate significantly increases in the subthreshold region. To investigate the reason for the MCU increase, the dependences of the MCU rate on the body-bias voltage and the distance between well ties are examined, and we conclude that the main cause of the MCU increase is not the parasitic bipolar effect but another mechanism, such as charge sharing. In addition, the dependence of the variation in the soft-error immunity of each memory cell on the supply voltage is examined, since SRAMs operating in the subthreshold region are sensitive to manufacturing variability. Measurement results indicate that the number of soft errors in each memory cell varies cell by cell, whereas the cause of the variation is explained by the spatial randomness of alpha-particle hits, and a distinct influence of manufacturing variability is not observed even in the subthreshold region.Index Terms-Alpha-particle-induced soft error, multiple-cell upset (MCU), soft-error rate (SER), subthreshold circuit.

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mentioning

confidence: 99%

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Fuketa

Harada

Hashimoto

et al. 2014

IEEE Trans. Device Mater. Relib.

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“…Recently, ultra dynamic voltage scaling (UDVS) has attracted unprecedented interest since energy efficiency is widely recognized as the topmost design criterion in numerous applications such as micro-processors, portable electronics, wireless sensor node, etc., where V MIN is a major design concern for power and energy saving during low-performance modes [9,10]. It has been corroborated that minimum energy consumption can be achieved in the sub-threshold or near-threshold region where current drivability is an exponential function of device threshold voltage and node voltage.…”

Section: Introductionmentioning

confidence: 92%

Impact Analysis of NBTI/PBTI on SRAM V_MINand Design Techniques for Improved SRAM V_MIN

Kim¹,

Kong²

2013

JSTS:Journal of Semiconductor Technology and Science

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“…Indeed, low-power processors devoted to bio-signal analysis have been proposed in the literature. Digital architectures in this domain mainly leverage aggressive Voltage-Frequency Scaling (VFS) [41] [2] [42], operation-level parallelism [19] [11] or application speci c accelerators [9] [26], in order to lower energy requirements. ese works consider a two-dimensional design space, tradingo costs (area and power) for performance (clock speed, instructions per cycle).…”

Section: Smart Wireless Body Sensor Nodesmentioning

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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Microwatt embedded processor platform for medical system-on-chip applications

Cited by 61 publications

References 13 publications

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Impact Analysis of NBTI/PBTI on SRAM V_MINand Design Techniques for Improved SRAM V_MIN

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

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Microwatt embedded processor platform for medical system-on-chip applications

Cited by 61 publications

References 13 publications

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Measurement and Analysis of Alpha-Particle-Induced Soft Errors and Multiple-Cell Upsets in 10T Subthreshold SRAM

Impact Analysis of NBTI/PBTI on SRAM VMINand Design Techniques for Improved SRAM VMIN

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

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Impact Analysis of NBTI/PBTI on SRAM V_MINand Design Techniques for Improved SRAM V_MIN