ADIC : Anomaly Detection Integrated Circuit in 65-nm CMOS Utilizing Approximate Computing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Bapi Kar
  • Pradeep Kumar Gopalakrishnan
  • Sumon Kumar Bose
  • Mohendra Roy
  • Arindam Basu

Detail(s)

Original languageEnglish
Article number9185089
Pages (from-to)2518-2529
Journal / PublicationIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume28
Issue number12
Online published2 Sep 2020
Publication statusPublished - Dec 2020
Externally publishedYes

Abstract

In this article, we present a low-power (LP) anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves LP operation through a combination of: 1) careful choice of algorithm for online learning and 2) approximate computing techniques to lower average energy. In particular, online pseudoinverse update method (OPIUM) is used to train a randomized neural network for quick and resource-efficient learning. An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference. Instead of a single classifier with large number of neurons, an ensemble of K base learner (BL) approach is chosen to reduce learning memory by a factor of K. This also enables approximate computing by dynamically varying the neural network size based on anomaly detection. Fabricated in 65-nm CMOS, the ADIC has = 7 BLs with 32 neurons in each BL and dissipates 11.87 and 3.35 pJ/OP during learning and inference, respectively, at Vdd = 0.75 V when all seven BLs are enabled. Furthermore, evaluated on the NASA bearing data set, approximately 80% of the chip can be shut down for 99% of the lifetime leading to an energy efficiency of 0.48 pJ/OP, an 18.5× reduction over full-precision computing running at Vdd = 1.2 V throughout the lifetime.

Research Area(s)

  • Anomaly detection, approximate computing, edge computing, energy savings, Internet of Things, one class classification, predictive maintenance (PdM)

Citation Format(s)

ADIC : Anomaly Detection Integrated Circuit in 65-nm CMOS Utilizing Approximate Computing. / Kar, Bapi; Gopalakrishnan, Pradeep Kumar; Bose, Sumon Kumar; Roy, Mohendra; Basu, Arindam.

In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 28, No. 12, 9185089, 12.2020, p. 2518-2529.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review