IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data fusion and sensing technology_IEEEISCAS2025

This paper presents the implementation of machine learning algorithms on GAP9 for real-time tactile data processing. The case study is object recognition based on multisensory glove data. A shallow 1D-CNN model was employed to first process the data, then deployed in the GAP SDK simulation environment (GVSoC), and various metrics were extracted to evaluate the GAP9 in this task. The results showed a latency of 85.37 μs. Additionally, memory profiling showed that no clock cycles were wasted waiting for pure I/O operations, since the model was allocated 23.98kB (20.7%) of the L1 cache and 13.96kB (1.07%) of L2 without any need for buffering between caches or external memory access operations. The GAP9, using our model, has a dynamic energy consumption of 6.76 pJ per inference. The results demonstrate the effectiveness of the proposed methodology, opening up an interesting perspective for various biomedical applications.