ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS

TL;DR

This paper introduces ADEPOS, an adaptive approximate computing framework for anomaly detection in IoT systems that significantly reduces energy consumption while maintaining detection accuracy, validated through a 65nm CMOS chip implementation.

Contribution

The paper presents a novel adaptive approximate computing method, ADEPOS, that dynamically adjusts neural network complexity for energy-efficient anomaly detection in IoT devices.

Findings

Achieves 8.95X energy savings over the system's lifetime.

Maintains detection accuracy despite energy-efficient approximations.

Demonstrates successful implementation in 65nm CMOS hardware.

Abstract

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.