Ultra Low Power Sensor Node for Security Applications, Facilitated by Algorithm-Architecture Co-design - Sharma, Saransh; Mukherjee, Avilash; Dongre, Abhishek; Sharad, Mrigank (VLSI design, 2017)
Design of an ultra-low power sensor node for identifying human trespassing is proposed, that can be attractive for security applications. Approximate Frequency Transformation (AFT) technique has been employed to characterize and classify the acoustic signals in order to identify sounds produced by human motion and human voice, under practical surrounding conditions. The approximate frequency spectrum is constructed using simple computations like the detection of zero-crossings and local peaks. The algorithm being memory-intensive mandates careful memory access scheme, along with optimum choice of classifying feature parameters. A custom designed Generalized Regression Neural Network (GRNN) block is used to classify the AFT results. The proposed design employs tight co-optimization of the algorithm and the corresponding computing architecture to achieve highly energy efficient "Edge-Computing" on the sensor node and hence, can facilitate deployment of large scale Wireless Sensor Network, with high node-density for security applications.