The implementation of tactile data decoding on low power embedded system is crucial for the development of e-skin systems that are body worn and supplied by batteries. These embedded electronic systems have to comply with severe constraints imposed by the application, especially real-time functionality battery lifetime. One of the main challenges it to find a sweet spot between power consumption and performance as tactile data decoding implementation requires high amount of computational power. In this paper, we present the assessment of the system energy efficiency of tactile data decoding using an ultra-low power parallel platform. The work performs tactile data decoding using a support vector machine (SVM) based tensor kernel algorithm for input touch modalities classification. To improve the energy efficiency the system use a parallel ultra-low power (PULP) processor that satisfies the computational demands of the e-skin system by decoding the data streams generated by an array of tactile sensors. PULP allows to meet the computational requirements of the target application, without exceeding the power envelope of a 150 mW needed to have a long-term wearable device. We present the whole platform supported by experimental results that show the benefits in terms of low power and computational performance. The proposed algorithm runs more than 9x faster than an ARM Cortex M4 at 168MHz with the same consumption.