Runtime Reconfigurable FPGA Accelerator for Tactile Texture Classification Based Shallow CNN

Electronic skin (e-skin) represents a transformative advancement in human-machine interaction, offering tactile sensing capabilities that emulate the mechanical and physiological properties of human skin. The integration of edge AI for this type of application enables sensors to process complex data in real time, but deploying AI models on resource-constrained embedded platforms remains a challenge due to limitations in memory, energy efficiency, and computational power. In this work, we present the case study of a 1D-CNN adaptive accelerator for texture recognition implemented on an FPGA, leveraging a design flow that offers support for the design and deployment of quantized CNN accelerators with runtime reconfiguration capabilities. As a step toward a future project that combines an FPGA with a tactile acquisition interface, we extended the design flow to support 1D-CNNs and subsequently analyzed the effects of quantization on the CNN accelerator’s precision, resource usage, and power consumption.