Enabling Plasticity in FPGA-Based SNNs: an EEG Seizure Detection Study

Real-time classification or anomaly detection tasks on sensor data often represent a significant challenge, due to signal variability and noise levels corrupting the data in unpredictable ways. This issue is especially relevant in biomedical applications, where data acquisition is noticeably unbalanced, inter-person and inter-day signal variability is non-negligible, and data annotation is an extremely complex task to be completed by expert physicians. In this context, Spiking Neural Networks (SNNs) provide the interesting opportunity of mimicking the learning capabilities of the brain through different plasticity mechanisms, which allow an online refinement of the model’s parameters in an unsupervised fashion. This work aims at leveraging plasticity to adjust on the fly the classification performance of a lightweight SNN model, targeting biological signal monitoring in the wearable domain, by relying on a specialized low-power SNN accelerator enabling signal classification within a power envelope of 0.25 mW. The efficacy of the approach is evaluated on a seizure recognition problem based on the real-time processing of the electroencephalography (EEG) signal, where ensuring a good trade-off between anomaly detection and false-alarms minimization is paramount, and seizure occurrences vary significantly. The plasticity rule is executed on the host RISC-V processor integrated in the FPGA-based accelerator, enabling efficient and self-adaptive always-on monitoring with a negligible impact on the system’s power consumption.