Extreme value estimates using vibration energy harvesting

This paper establishes the possibility of utilizing energy harvesting from mechanical vibrations to estimate extreme value responses of the host structure and demonstrates the calibration of these estimates for excitation spectra typical to natural systems. For illustrative purposes, a cantilever type energy harvester is considered for wind excitation. The extreme value estimates are established through a Generalized Pareto Distribution (GPD). Classically well-known Kaimal and Davenport spectra for wind have been considered in this paper for comparison purposes. The applicability of GPD for processes with short-range dependence is explored in both linear and nonlinear systems. The work also demonstrates how return levels can be mapped using energy harvesting levels and indicates that vibration energy harvesting, in its own right, has the potential to be used for extreme value analysis and estimates. The work has impact on health monitoring and assessment of built infrastructure in various stages of repair or disrepair and exposed to nature throughout their lifetime

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community

Ising Machines and Spiking Neural Networks: Non von-Neumann Computing using Networks of Coupled Oscillators

Computation has become synonymous with digital computation in the 21st century. The exponential growth in computational demand has been identical in trend to the exponential growth of compute resources available on a chip. However, recently the latter has started showing signs of slowing down as the physical limits are approached of the semiconducting substrates that have continually allowed transistor miniaturization for decades. This has brought into scrutiny the foundational architecture of general purpose computers established at the advent of the digital computer - the von Neumann architecture. Separating data and instructions, the von-Neumann architecture has been a simple yet highly successful model of computation. However, many classes of problems such as 'cognitive tasks' in image recognition, anomaly detection, speech recognition, etc. require very high throughput of data and are sub-optimally served by this architecture. Another class of problems is combinatorial optimization where a the optimal solution to a function with a large discrete configuration space is to be found. The exponential growth in the number of configurations even for moderate real-world problems makes this class among the hardest for traditional computers to solve. This thesis explores the potential of using coupled analog oscillators to construct non-von-Neumann architectures for such tasks where a significant advantage may be gained over traditional models. To solve combinatorial optimization, the concept of 'Ising Machines' has been proposed where dynamics of coupled oscillators is adapted to find the solution of the problem as the lowest energy state of a physical system. The Ising machine paradigm is studied using a general coupled oscillator model including amplitude dynamics for the first time. A control scheme called 'Parametric cycling' is proposed that prevents local minima traps to a significant extent. The performance is demonstrated on Max-Cut problems of fully connected graphs and cubic graphs. Further, drawing on the neuroscientific abstraction of neurons in the brain as coupled oscillators, a neuromorphic architecture is constructed suitable for anomaly detection problems. Spiking Neural Networks are proposed to solve structural health monitoring problems by extracting cepstral coefficients as features. The implementation is tested on a novel hardware platform named Intel Loihi, offering the potential of highly energy efficient operation of neuromorphic algorithms. This thesis showcases the computational potential of oscillators beginning with a single resonator, a piezoelectric energy harvester, which may be used to estimate extreme values of responses and fragility of structures. It progresses to coupled (Stuart-Landau) oscillators and demonstrates combinatorial optimization capability. Finally, coupled integrate-and-fire oscillators or spiking neurons are used to detect damage induced anomalies in the vibration response of a structure. The research presented in this thesis thus establishes coupled oscillator networks as prime contenders for computing primitives of the future in classes of problems where digital computers based on the von-Neumann architecture are fundamentally constrained

Spiking Neural Networks for Structural Health Monitoring

This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach

Fragility analysis using vibration energy harvesters

Fragility curves are widely used as indicators of vulnerability of infrastructure to structural/performance demands posed on it by the environment. Fragility of a structure may change due to variety of factors during its lifetime. This renders the applicability of fragility for risk assessment to be reliant on periodically updated estimation. Derivation of fragility requires an estimate of the capacity of the structure and the demand due to external factors. Vibration data can be used as a medium to estimate both capacity and demand on the system. Energy harvesters being self-sufficient vibration sensors are proposed as devices capable of estimating fragility curves in lieu of other inertial sensors. The concept is illustrated here using simplified models. The reduction in system complexity due to the self-powered nature of the energy harvester along with the proposed ability to compute probability of failure make energy harvesters attractive options for monitoring civil infrastructure and thereby minimizes risk at various stages in its lifetime