  To overcome the energy and bandwidth limitations of traditional IoT systems,
edge computing or information extraction at the sensor node has become popular.
However, now it is important to create very low energy information extraction
or pattern recognition systems. In this paper, we present an approximate
computing method to reduce the computation energy of a specific type of IoT
system used for anomaly detection (e.g. in predictive maintenance, epileptic
seizure detection, etc). Termed as Anomaly Detection Based Power Savings
(ADEPOS), our proposed method uses low precision computing and low complexity
neural networks at the beginning when it is easy to distinguish healthy data.
However, on the detection of anomalies, the complexity of the network and
computing precision are adaptively increased for accurate predictions. We show
that ensemble approaches are well suited for adaptively changing network size.
To validate our proposed scheme, a chip has been fabricated in UMC65nm process
that includes an MSP430 microprocessor along with an on-chip switching mode
DC-DC converter for dynamic voltage and frequency scaling. Using NASA bearing
dataset for machine health monitoring, we show that using ADEPOS we can achieve
8.95X saving of energy along the lifetime without losing any detection
accuracy. The energy savings are obtained by reducing the execution time of the
neural network on the microprocessor.
