The time interval between consecutive heartbeats (interpulse interval, IPI) has previously been suggested for securing mobile-health solutions. This time interval is known to contain a degree of randomness, permitting the generation of a time- and person-specific identifier. It is commonly assumed that only devices trusted by a person can make physical contact with him/her, and that this physical contact allows each device to generate a similar identifier based on its own cardiac recordings. Under these conditions, the identifiers generated by different trusted devices can facilitate secure authentication. Recently, a wide range of techniques have been proposed for measuring heartbeats remotely, a prominent example of which is remote photoplethysmography (rPPG). These techniques may pose a significant threat to heartbeat-based security, as an adversary may pretend to be a trusted device by generating a similar identifier without physical contact, thus bypassing one of the core security conditions. In this paper, we assess the feasibility of such remote attacks using state-of-the-art rPPG methods. Our evaluation shows that rPPG has similar accuracy as contact PPG and, thus, forms a substantial threat to heartbeat-based-security systems that permit trusted devices to obtain their identifiers from contact PPG recordings. Conversely, rPPG cannot obtain an accurate representation of an identifier generated from electrical cardiac signals, making the latter invulnerable to state-of-the-art remote attacks.

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. Main results. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects that should be considered in practice, including subjects with reduced heart-rate variability (HRV), different sensor-sampling frequencies, intersensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-case-authentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, less-entropic keys may actually increase the key strength by reducing the effects of intersensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's HRV. To improve security, we introduce the inter-multi-pulse interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between nonconsecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4× (+19.2 bits) for subjects with limited HRV, at the cost of an extended key-generation time of 4.8× (+45 s).

This article presents a chip multiprocessor (CMP) design that mixes coarse- and fine-grained reconfigurability to increase core availability of safety-critical embedded systems in the presence of hard errors. The authors conducted a comprehensive design-space exploration to identify the granularity mixes that maximize CMP fault tolerance and minimize performance and energy overheads. The authors added fine-grained reconfigurable logic to a coarse-grained sparing approach. Their resulting design can tolerate 3 times more hard errors than core redundancy and 1.5 times more than any other purely coarse-grained solution.

In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.

The Horizon 2020 SHARCS project is a framework for designing, building and demonstrating secure-by-design applications and services, that achieve end-to-end security for their users. In this paper we present the basic elements of SHARCS that will provide a powerful foundation for designing and developing trustworthy, secure-by-design applications and services for the Future Internet.

The Inter-Pulse-Interval (IPI) of heart beats has previously been suggested for facilitating security in mobile health (mHealth) applications. In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats. As two entities that simultaneously sample the same heart beats may generate the same key (with some inter-key disparity), these keys may be used for various security functions, such as entity authentication or data confidentiality. One of the key limitations in heart-beat-based security is the low randomness intrinsic to the most-significant bits (MSBs) in the digital representation of each IPI. In this paper, we explore the use of a von Neumann entropy extractor on these MSBs in order to increase their randomness. We show that our von Neumann key-generator produces significantly more random bits than a non-extracting key generator with an average bit-extraction rate between 13.4% and 21.9%. Despite this increase in randomness, we also find a substantial increase in inter-key disparity, increasing the mismatch tolerance required for a given true-key pair. Accordingly, the maximum-attainable effective key-strength of our key generator is only slightly higher than that of a non-extracting generator (16.4 bits compared to 15.2 bits of security for a 60-bit key), while the former requires an increase in average key-generation time of 2.5x.

The Inter-Pulse-Interval (IPI) of heart beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communication is facilitated through a security key derived from the time difference between heart beats. However, there currently exists no work which considers the effect on security of imperfect heart-beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9% and 99.0%, a significant drop in security performance may be observed (between -70% and -303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect heart beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%.

Recent trends in semiconductor technology have dictated the constant reduction of device size. One negative effect stemming from the reduction in size and increased complexity is the reduced device reliability. This paper is centered around the matter of permanent fault tolerance and graceful system degradation in the presence of permanent faults. We take advantage of the natural redundancy of homogeneous multicores following a sparing strategy to reuse functional pipeline stages of faulty cores. This is done by incorporating reconfigurable interconnects next to which the cores of the system are placed, providing the flexibility to redirect the data-flow from the faulty pipeline stages of damaged cores to spare (still) functional ones. Several micro-architectural changes are introduced to decouple the processor stages and allow them to be interchangeable. The proposed approach is a clear departure from previous ones by offering full flexibility as well as highly graceful performance degradation at reasonable costs. More specifically, our coarsegrain faulttolerant multicore array provides up to ×4 better availability compared to a conventional multicore and up to ×2 higher probability to deliver at least one functioning core in high fault densities. For our benchmarks, our design (synthesized for STM 65nm SP technology) incurs a total execution-time overhead for the complete system ranging from ×1.37 to ×3.3 compared to a (baseline) non-fault-tolerant system, depending on the permanent-fault density. The area overhead is 19.5% and the energy consumption, without incorporating any power/energy- saving technique, is estimated on average to be 20.9% higher compared to the baseline, unprotected design.

The Inferior-Olivary nucleus (ION) is a well-charted brain region, heavily associated with the sensorimotor control of the body. It comprises neural cells with unique properties which facilitate sensory processing and motor-learning skills. Simulations of such neurons become rapidly intractable when biophysically plausible models and meaningful network sizes (at least in the order of some hundreds of cells) are modeled. To overcome this problem, we accelerate a highly detailed ION network model using a Maxeler Dataflow Computing Machine. The design simulates a 330-cell network at real-time speed and achieves maximum throughputs of 24.7 GFLOPS. The Maxeler machine, integrating a Virtex-6 FPGA, yields speedups of ×92-102, and ×2-8 compared to a reference-C implementation, running on a Intel Xeon 2.66GHz, and a pure Virtex-7 FPGA implementation, respectively.

The DeSyRe project builds on-demand adaptive, reliable Systems-on-Chips. In response to the current semiconductor technology trends thatmake chips becoming less reliable, DeSyRe describes a newgeneration of by design reliable systems, at a reduced power and performance cost. This is achieved through the following main contributions. DeSyRe defines a fault-tolerant system architecture built out of unreliable components, rather than aiming at totally fault-free and hence more costly chips. In addition, DeSyRe systems are on-demand adaptive to various types and densities of faults, as well as to other system constraints and application requirements. For leveraging on-demand adaptation/customization and reliability at reduced cost, a new dynamically reconfigurable substrate is designed and combined with runtime system software support. The above define a generic and repeatable design framework, which is applied to two medical SoCs with high reliability constraints and diverse performance and power requirements. One of the main goals of the DeSyRe project is to increase the availability of SoC components in the presence of permanents faults, caused at manufacturing time or due to device aging. A mix of coarse- and fine-grain reconfigurable hardware substrate is designed to isolate and bypass faulty component parts. The flexibility provided by the DeSyRe reconfigurable substrate is exploited at runtime by system optimization heuristics,which decide tomodify component configurationwhen a permanent fault is detected, providing graceful degradation.

The Inferior-Olivary nucleus (ION) is a well-charted region of the brain, heavily associated with sensorimotor control of the body. It comprises ION cells with unique properties which facilitate sensory processing and motor-learning skills. Various simulation models of ION-cell networks have been written in an attempt to unravel their mysteries. However, simulations become rapidly intractable when biophysically plausible models and meaningful network sizes (100 cells) are modeled. To overcome this problem, in this work we port a highly detailed ION cell network model, originally coded in Matlab, onto an FPGA chip. It was first converted to ANSI C code and extensively profiled. It was, then, translated to HLS C code for the Xilinx Vivado toolflow and various algorithmic and arithmetic optimizations were applied. The design was implemented in a Virtex 7 (XC7VX485T) device and can simulate a 96-cell network at real-time speed, yielding a speedup of 700 compared to the original Matlab code and 12.5 compared to the reference C implementation running on a Intel Xeon 2.66GHz machine with 20GB RAM. For a 1,056-cell network (non-real-time), an FPGA speedup of 45 against the C code can be achieved, demonstrating the design's usefulness in accelerating neuroscience research. Limited by the available on-chip memory, the FPGA can maximally support a 14,400-cell network (non-real-time) with online parameter configurability for cell state and network size. The maximum throughput of the FPGA IONnetwork accelerator can reach 2.13 GFLOPS.

Recent work on wireless Implantable Medical Devices (IMDs) has revealed the need for secure communication in order to prevent data theft and implant abuse by malicious attackers. However, security should not be provided at the cost of patient safety and an IMD should, thus, remain accessible during an emergency regardless of device security. In this paper, we present a novel method of providing IMD emergency access, based on generating Entity Identifiers (EI) using the Inter-Pulse Intervals (IPIs) of heartbeats. We evaluate the current state-of-the-art in EI-generation in terms of security and accessibility for healthy subjects with a wide range of heart rates. Subsequently, we present an adaptive EI-generation algorithm which takes the heart rate into account, maintaining an acceptable emergency-mode activation time (between 5-55.4 s) while improving security by up to 3.4x for high heart rates. Finally, we show that activating emergency mode may consume as little as 0.24μJ from the IMD battery.

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect-/fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints.

In this article, new heuristic-search methods and algorithms are presented for enabling highly efficient and adaptive, defect-tolerant multiprocessor arrays. We consider systems where a homogeneous multiprocessor array lies on top of reconfigurable interconnects which allow the pipeline stages of the processors to be connected in all possible configurations. Considering the multiprocessor array partitioned in substitutable units at the granularity of pipeline stages, we employ a variety of heuristic-search methods and algorithms to isolate and replace defective units. The proposed heuristics are designed for off-line execution and aim at minimizing the performance overhead necessarily introduced to the array by the interconnects' latency. An empirical evaluation of the designed algorithms is then carried out, in order to assess the targeted problem and the efficacy of our approach. Our findings indicate this to be a NP-complete computational problem, however, our heuristic-search methods can achieve, for the problem sizes we exhaustively searched, 100% accuracy in finding the optimal solution among 1019 possible candidates within 2.5 seconds. Alternatively, they can provide near-optimal solutions at an accuracy which consistently exceeds 70% (compared to the optimal solution) in only 10-4 seconds.

In this paper, we describe a generic approach for integrating a dynamically reconfigurable device into a general purpose system interconnected with a high-speed link. The system can dynamically install and execute hardware instances of functions to accelerate parts of a given software code. The hardware descriptions of the functions (bitstreams) are inserted into the executable binary running on the system. Our compiler further inserts system-calls to the software code to control the reconfigurable device. Thereby, the general purpose host-processor of the system manages the hardware reconfiguration and execution through a Linux device driver. The device has direct access to the main memory (DMA) operating in the virtual address space; it further supports memory mapped IO for data and control, and is able to raise and handle interrupts for synchronization. The above system is implemented on a general purpose machine providing a HyperTransport bus to connect a Xilinx Virtex4-100 FPGA, an AMD Opteron-244, and 1 GB of DDR main memory. We evaluate our proposal using a secure audio processing application. We accelerate in hardware the Audio processing kernel as well as the subsequent AES encryption function via dynamic partial self-reconfiguration. The proposed system achieves a 12 speedup over a software for the application at hand.