Neuromorphic Tactile Sensing System for Textural Features Classification

Artificial tactile sensing systems have gained significant attention in recent years due to their potential to enhance human-machine interaction. Numerous initiatives have been introduced to shift the computational paradigms of these systems towards a more biologically inspired approach, by incorporating neuromorphic computing methods. Despite the significant advances made by these systems, dependence on complex offline methods for classification (i.e. hand-crafted encoding features), remains a limitation for their real-time deployment. In this work, we present a neuromorphic tactile PVDF-based sensing system for textural features classification, that employs raw signals directly for classification. We first converted raw signals into spikes and then trained recurrent spiking neural networks (RSNNs) using Backpropagation Through Time (BPTT) with surrogate gradients to perform classification. We proposed an optimization method based on tuning the refractory period of the encoding neurons, to explore a potential trade-off between the computational cost and classification accuracy of the RSNN. The proposed method effectively identified two RSNNs with refractory period configurations that achieved a trade-off between the two evaluation metrics. Following this, we reduced the inference time steps of the selected RSNN during inference using a rate-coding based method. This method succeeded in saving around 26.6% out of the total original time steps. In summary, the proposed system paves the way for establishing an end-to-end neuromorphic approach for tactile textural features classification, through deploying the selected RSNNs on a dedicated neuromorphic hardware device for real-time inferences.