Name
Abstract | ||
|---|---|---|
Electronic skin (e-skin) is playing a key role in medicine, prosthetic and robot applications. E-skin tries to sense the world like the human skin does, exploiting arrays of sensors that produce a huge amount of data to be processed. Processing tactile data close to the e-skin imposes big challenges in terms of energy efficiency and real-time functionality. Energy efficient is particularly important for wearable and prosthetic applications where e-skin is mainly used as a wearable device supplied by battery. The major challenges it to find a sweet spot between power consumption and performance as tactile data decoding implementation requires high amount of computational power. This paper presents the hardware-software implementation of a complete low power embedded system that matches the computational requirements and the energy efficiency exploiting an ultra-low power parallel processor. Tactile data decoding is directly performed on the embedded system, with a support vector machine (SVM) based tensor kernel algorithm, which classifies the input touch modalities. The paper presents the implementation of the algorithm in details and the power performance of the whole system based on experimental evaluation. Experimental results show that the energy efficiency is 9 times more than system based on a low power ARM-Cortex M4. Finally, the simulation on the energy consumption demonstrated that the developed system is able to last for 19.8 h in continuous mode with a single 2 Ah Lithium Polymer battery. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1166/jolpe.2018.1537 | JOURNAL OF LOW POWER ELECTRONICS |
Keywords | Field | DocType |
Electronic Skin, Parallel Ultra-Low Power, Machine Learning Implementation, Low Power Processing, Wearable Devices | Efficient energy use,Engineering,Decoding methods,Embedded system | Journal |
Volume | Issue | ISSN |
14 | 1 | 1546-1998 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Magno | 1 | 500 | 59.74 |
| Ali Ibrahim | 2 | 71 | 12.23 |
| Antonio Pullini | 3 | 390 | 28.27 |
| M. Valle | 4 | 97 | 19.19 |
| Luca Benini | 5 | 13116 | 1188.49 |