Efficient machine learning algorithm for embedded tactile data processing

Employing Machine learning algorithms in tactile sensing systems have emerged recently to recognize/classify touch patterns. The high computational complexity of the ML algorithms makes challenging the embedded implementation of tactile data processing. This paper proposes a complexity optimized tensorial-based machine learning algorithm for touch modality classification. The aim is to introduce an efficient algorithm minimizing the system complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the state of the art, the proposed approach reduces the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43x at a cost of only 2% loss in accuracy.