TY - GEN
T1 - Low complexity single channel ICA architecture design methodology for pervasive healthcare applications
AU - Bhardwaj, Swati
AU - Bhagyaraja, Adapa
AU - Shashank, R.
AU - Jadhav, Pranit
AU - Biswas, Dwaipayan
AU - Acharyya, Amit
AU - Naik, Ganesh R.
PY - 2016/12/12
Y1 - 2016/12/12
N2 - In this paper, we propose a low-complexity architecture design methodology for the Single Channel Independent Component Analysis (SCICA) algorithm targeting pervasive personalized healthcare. SCICA, unlike the conventional ICA, separates the signal from multiple sources using only a single sensor that has tremendous potential for reducing the number of body-worn sensors. However, such applications are constrained by power consumption limitation due to the battery backup necessitating low-complexity system design and the on-chip area requirement. On the other hand, SCICA, involving computationally intensive stages including ICA, Fast Fourier Transform (FFT), Eigen Value Decomposition (EVD) and k-means clustering, is not possible to be mapped onto the low-complexity architecture directly from the algorithmic level. Hence, in this paper, adopting algorithm-architecture holistic approach, we introduce the Coordinate Rotation Digital Computer (CORDIC) based low-complexity SCICA architecture design methodology suitable for such resource constrained applications. K-means architecture used for low-complex SCICA based on the proposed methodology consumes core silicon area of 0.28mm2 and power of 0.25mW at 1.2 V, 1-MHz frequency using 0.13μm standard cell technology library (TSMC) that is about 50% less than that of the state-of-the art approaches. The functionality has been compared favorably with the conventional SCICA and hardware analysis has also cross-verified the lowcomplexity nature of the proposed methodology.
AB - In this paper, we propose a low-complexity architecture design methodology for the Single Channel Independent Component Analysis (SCICA) algorithm targeting pervasive personalized healthcare. SCICA, unlike the conventional ICA, separates the signal from multiple sources using only a single sensor that has tremendous potential for reducing the number of body-worn sensors. However, such applications are constrained by power consumption limitation due to the battery backup necessitating low-complexity system design and the on-chip area requirement. On the other hand, SCICA, involving computationally intensive stages including ICA, Fast Fourier Transform (FFT), Eigen Value Decomposition (EVD) and k-means clustering, is not possible to be mapped onto the low-complexity architecture directly from the algorithmic level. Hence, in this paper, adopting algorithm-architecture holistic approach, we introduce the Coordinate Rotation Digital Computer (CORDIC) based low-complexity SCICA architecture design methodology suitable for such resource constrained applications. K-means architecture used for low-complex SCICA based on the proposed methodology consumes core silicon area of 0.28mm2 and power of 0.25mW at 1.2 V, 1-MHz frequency using 0.13μm standard cell technology library (TSMC) that is about 50% less than that of the state-of-the art approaches. The functionality has been compared favorably with the conventional SCICA and hardware analysis has also cross-verified the lowcomplexity nature of the proposed methodology.
KW - CORDIC
KW - EVD
KW - FFT
KW - K-means clustering
KW - Pervasive healthcare
KW - SCICA
UR - http://www.scopus.com/inward/record.url?scp=85013168631&partnerID=8YFLogxK
U2 - 10.1109/SiPS.2016.15
DO - 10.1109/SiPS.2016.15
M3 - Conference contribution
AN - SCOPUS:85013168631
SN - 9781509033621
T3 - IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
SP - 39
EP - 44
BT - 2016 IEEE International Workshop on Signal Processing Systems (SiPS)
PB - IEEE
CY - Texas
T2 - 2016 IEEE International Workshop on Signal Processing Systems, SiPS 2016
Y2 - 26 October 2016 through 28 October 2016
ER -