Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial sensors. We propose a complete pipeline to benchmark these encoding techniques by performing time-dependent signal classification through a Spiking Convolutional Neural Network (sCNN), including a signal preprocessing step consisting of a bank of filters inspired by the human cochlea, feature extraction by production of a sonogram, transfer learning via an equivalent ANN, and model compression schemes aimed at resource optimization. The resulting performance comparison and analysis provides a powerful practical tool, empowering developers to select the most suitable coding method based on the type of data and the desired processing algorithms, and further expands the applicability of neuromorphic computational paradigms to embedded sensor systems widely employed in the IoT and industrial domains

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications

First steps towards micro-benchmarking the Lava-Loihi neuromorphic ecosystem

Neuromorphic computing has proved to be capable of remarkable gains in energy efficiency over traditional architectures. With the continuous development of new software and hardware tools, benchmarking plays a crucial role in the measurement of technological advancement in the field. Over the last few years, some benchmarking efforts for neuromorphic computing have been proposed focusing on specific tasks like noise suppression or gesture recognition, while micro-benchmarking approaches have not been widely investigated. In this paper, we present our proposal to fill this gap: the Lava micro-benchmarking suite, a set of tests specifically designed for the Lava neuromorphic framework and the Loihi 2 neuromorphic architecture. Tests are divided into two broad categories: those aimed at evaluating Lava's message-passing implementation, that are partially inspired by MPI benchmarks, and those specifically designed to test the Loihi 2 architecture and the Lava compilation process. The suite is still a work in progress that needs to be extended to cover more functionalities of Lava and Loihi 2, but in its current state is functional and includes tests covering the three main hardware backends of interest: host CPU, Loihi 2 embedded CPU and Loihi 2 neuron cores. We present the general software design of the suite, the methods we have used to implement the tests and collect measurements and some examples of results obtained from running the tests. We expect that the suite can help Lava / Loihi 2 developers and users with meaningful insights that can be used to improve the state of this neuromorphic ecosystem

Black-Box Modelling of Low-Switching-Frequency Power Inverters for EMC Analyses in Renewable Power Systems

Electromagnetic interference (EMI) from renewable power systems to the grid attracts more attention especially in the low-frequency range, due to the low switching frequency of high-power inverters. It is significantly important to derive EMI models of power inverters as well as to develop strategies to suppress the related conducted emissions. In this work, black-box modelling is applied to a three-phase inverter system, by implementing an alternative procedure to identify the parameters describing the active part of the model. Besides, two limitations of black-box modelling are investigated. The first regards the need for the system to satisfy the linear and time-invariant (LTI) assumption. The influence of this assumption on prediction accuracy is analysed with reference to the zero, positive and negative sequence decomposition. It is showing that predictions for the positive/negative sequence are highly influenced by this assumption, unlike those for the zero sequence. The second limitation is related to the possible variation of the mains impedance which is not satisfactorily stabilized at a low frequency outside the operating frequency range of standard line impedance stabilization networks