Date: Wednesday 26 March 2014
Time: 14:30 - 16:00
Location / Room: Konferenz 1

Chair:
Giovanni Ansaloni, EPFL, CH

Co-Chair:
Andrea Bartolini, University of Bologna, IT

Achieving low power operation is essential for battery operated mobile health applications. In this session, the papers address this important issue. The first two papers present multicore architectural methods for bio-signal processing, dealing with synchronisation and innovative memory architecture design. The last two papers focus on low power design of applications for bio-signal processing: tuning of sensor usage based on applications and methods to selectively drop computations to save power, without affecting the accuracy.

Time	Label	Presentation Title Authors
14:30	7.3.1	HARDWARE/SOFTWARE APPROACH FOR CODE SYNCHRONIZATION IN LOW-POWER MULTI-CORE SENSOR NODES Speakers: Rubén Braojos1, Ahmed Dogan2, Ivan Beretta2, Giovanni Ansaloni2 and David Atienza2 1École Polytechnique Fédérale de Lausanne, CH; 2EPFL, CH Abstract Latest embedded bio-signal analysis applications, targeting low-power Wireless Body Sensor Nodes (WBSNs), present conflicting requirements. On one hand, bio-signal analysis applications are continuously increasing their demand for high computing capabilities. On the other hand, long-term signal processing in WBSNs must be provided within their highly constrained energy budget. In this context, parallel processing effectively increases the power efficiency of WBSNs, but only if the execution can be properly synchronized among computing elements. To address this challenge, in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs. This new approach requires little hardware resources and very few adaptations in the source code. Moreover, it provides the necessary flexibility to execute applications with an arbitrarily large degree of complexity and parallelism, enabling considerable reductions in power consumption for all multi-core WBSN execution conditions. Experimental results show that a multi-core WBSN architecture using the illustrated approach can obtain energy savings of up to 40%, with respect to an equivalent single-core architecture, when performing advanced bio-signal analysis.
15:00	7.3.2	HYBRID MEMORY ARCHITECTURE FOR VOLTAGE SCALING IN ULTRA-LOW POWER MULTI-CORE BIOMEDICAL PROCESSORS Speakers: Daniele Bortolotti1, Andrea Bartolini1, Christian Weis2, Davide Rossi1 and Luca Benini1 1University of Bologna, IT; 2University of Kaiserslautern, DE Abstract Technology scaling enables today the design of sensor-based ultra-low cost chips well suited for emerging applications such as wireless body sensor networks, urban life and environment monitoring. Energy consumption is the key limiting factor of this up-coming revolution and memories are often the energy bottleneck mainly due to leakage power. This paper proposes an ultra-low power multi-core architecture targeting eHealth monitoring systems, where applications involve collection of sequences of slow biomedical signals and highly parallel computations at very low voltage. We propose a hybrid memory architecture that combines 6T-SRAM and 8T-SRAM operating in the same voltage domain and capable of dispatching at high voltage a normal operation and at low voltage a fully reliable small memory partition (8T) while the rest of the memory (6T) is state-retentive. Our architecture offers significant energy savings with a low area overhead in typical eHealth Compressed Sensing-based applications.
15:30	7.3.3	CONTEXT AWARE POWER MANAGEMENT FOR MOTION-SENSING BODY AREA NETWORK NODES Speakers: Filippo Casamassima1, Elisabetta Farella2 and Luca Benini3 1University of Bologna, IT; 2DEI - University of Bologna, IT; 3Università di Bologna, IT Abstract Body Area Networks (BANs) are widely used mainly for healthcare and fitness purposes. In both cases, the lifetime of sensor nodes included in the BAN is a key aspect that may affect the functionality of the whole system. Typical approaches to power management are based on a trade-off between the data rate and the monitoring time. Our work introduces a power management layer capable to opportunistically use data sampled by sensors to detect contextual information such as user activity and adapt the node operating point accordingly. The use of this layer has been demonstrated on a commercial sensor node, increasing its battery lifetime up to a factor of 5.
15:45	7.3.4	A QUALITY-SCALABLE AND ENERGY-EFFICIENT APPROACH FOR SPECTRAL ANALYSIS OF HEART RATE VARIABILITY Speakers: Georgios Karakonstantis1, Aviinaash Sankaranarayanan2, Mohamed Sabry1, David Atienza1 and Andreas Burg1 1EPFL, CH; 2Debiotech S.A., CH Abstract Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case.
16:00	IP3-10, 633	BATTERY AWARE STOCHASTIC QOS BOOSTING IN MOBILE COMPUTING DEVICES Speakers: Hao Shen, Qiuwen Chen and Qinru Qiu, Syracuse University, US Abstract Mobile computing has been weaved into everyday lives to a great extend. Their usage is clearly imprinted with user's personal signature. The ability to learn such signature enables immense potential in workload prediction and resource management. In this work, we investigate the user behavior modeling and apply the model for energy management. Our goal is to maximize the quality of service (QoS) provided by the mobile device (i.e., smartphone), while keep the risk of battery depletion below a given threshold. A Markov Decision Process (MDP) is constructed from history user behavior. The optimal management policy is solved using linear programing. Simulations based on real user traces validate that, compared to existing battery energy management techniques, the stochastic control performs better in boosting the mobile devices' QoS without significantly increasing the chance of battery depletion.
16:00		End of session Coffee Break in Exhibition Area On Tuesday-Thursday the coffee and lunch breaks will be located in the Exhibition Area (Terrace Level).