Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

Spiking neural networks on FPGA: A survey of methodologies and recent advancements

The mimicry of the biological brain's structure in information processing enables spiking neural networks (SNNs) to exhibit significantly reduced power consumption compared to conventional systems. Consequently, these networks have garnered heightened attention and spurred extensive research endeavors in recent years, proposing various structures to achieve low power consumption, high speed, and improved recognition ability. However, researchers are still in the early stages of developing more efficient neural networks that more closely resemble the biological brain. This development and research require suitable hardware for execution with appropriate capabilities, and field-programmable gate array (FPGA) serves as a highly qualified candidate compared to existing hardware such as central processing unit (CPU) and graphics processing unit (GPU). FPGA, with parallel processing capabilities similar to the brain, lower latency and power consumption, and higher throughput, is highly eligible hardware for assisting in the development of spiking neural networks. In this review, an attempt has been made to facilitate researchers' path to further develop this field by collecting and examining recent works and the challenges that hinder the implementation of these networks on FPGA.

Sparse Spike Feature Learning to Recognize Traceable Interictal Epileptiform Spikes

Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated. However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy neurosurgery. Therefore, identifying spike subtypes that are strongly associated with epileptogenic focus (traceable spikes) could facilitate their use as reliable signal sources for accurately tracing epileptogenic focus. However, the sparse firing phenomenon in the transmission of intracranial neuronal discharges leads to differences within spikes that cannot be observed visually. Therefore, neuro-electro-physiologists are unable to identify traceable spikes that could accurately locate epileptogenic focus. Herein, we propose a novel sparse spike feature learning method to recognize traceable spikes and extract discrimination information related to epileptogenic focus. First, a multilevel eigensystem feature representation was determined based on a multilevel feature representation module to express the intrinsic properties of a spike. Second, the sparse feature learning module expressed the sparse spike multi-domain context feature representation to extract sparse spike feature representations. Among them, a sparse spike encoding strategy was implemented to effectively simulate the sparse firing phenomenon for the accurate encoding of the activity of intracranial neurosources. The sensitivity of the proposed method was 97.1%, demonstrating its effectiveness and significant efficiency relative to other state-of-the-art methods.

Methodology based on spiking neural networks for univariate time-series forecasting

Spiking Neural Networks (SNN) are recognised as well-suited for processing spatiotemporal information with ultra-low energy consumption. However, proposals based on SNN for classification tasks are more common than for forecasting problems. In this sense, this paper presents a new general training methodology for univariate time-series forecasting based on SNN. The methodology is focused on one-step ahead forecasting problems and combines a PulseWidth Modulation based encoding-decoding algorithm with a Surrogate Gradient method as supervised training algorithm. In order to validate the generality of the presented methodology sine-wave, 3 UCI and 1 available real-world datasets are used. The results show very satisfactory forecasting results (MAE∈[0.0094,0.2891]) regardless of the characteristics of the dataset or the application field. In addition, weights can be initialised just once to achieve robust results, boosting the advantages of computational and energy cost of SNN.

Comparison and Selection of Spike Encoding Algorithms for SNN on FPGA

The information in Spiking Neural Networks (SNNs) is carried by discrete spikes. Therefore, the conversion between the spiking signals and real-value signals has an important impact on the encoding efficiency and performance of SNNs, which is usually completed by spike encoding algorithms. In order to select suitable spike encoding algorithms for different SNNs, this work evaluates four commonly used spike encoding algorithms. The evaluation is based on the FPGA implementation results of the algorithms, including calculation speed, resource consumption, accuracy, and anti-noiseability, so as to better adapt to the neuromorphic implementation of SNN. Two real-world applicaitons are also used to verify the evaluation results. By analyzing and comparing the evaluation results, this work summarizes the characteristics and application range of different algorithms. In general, the sliding window algorithm has relatively low accuracy and is suitable for observing signal trends. Pulsewidth modulated-Based algorithm and step-forward algorithm are suitable for accurate reconstruction of various signals except for square wave signals, while Ben's Spiker algorithm can remedy this. Finally, a scoring method that can be used for spiking coding algorithm selection is proposed, which can help to improve the encoding efficiency of neuromorphic SNNs.