BioNetExplorer: Architecture-Space Exploration of Biosignal Processing Deep Neural Networks for Wearables

Bharath Srinivas Prabakaran, Asima Akhtar, Semeen Rehman, Osman Hasan, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review


Deep learning (DL) has been shown to be highly effective in solving various problems across numerous applications and domains, such as autonomous driving and image recognition. Due to the advent of DL, plenty of research works have explored the applicability of DL, more specifically deep neural networks (DNNs), to solve pattern recognition and computer vision challenges. More recently, researchers have focused on the topic of automated generation and exploration of DNN architectures, which tend to mostly focus on image recognition or visual data sets, primarily, due to the computer vision-related DL advancements. In this work, we propose the BioNetExplorer framework to systematically generate and explore multiple DNN architectures for biosignal processing in wearable devices. Our framework varies key neural architecture parameters to search for an embedded DNN architecture with a low hardware overhead, which can be deployed in wearable edge devices to analyze the biosignal data and to extract the relevant information, such as arrhythmia and seizure. Furthermore, BioNetExplorer reduces the exploration time by deploying genetic algorithms, such as NSGA-II, SPEA-2, etc. Our framework also enables the hardware-aware DNN architecture search by imposing user requirements and hardware constraints (storage, FLOPs, etc.) during the exploration stage, thereby limiting the number of networks explored. Moreover, BioNetExplorer can also be used to search for DNNs based on the user-required output classes; for instance, a user might require a specific output class, attributed toward ventricular fibrillation, due to genetic predisposition or a preexisting heart condition. The use of genetic algorithms reduces the exploration time, on average, by 9\times , compared to exhaustive exploration. We are successful in identifying Pareto-optimal designs, which can reduce the storage overhead of DNN by \sim 30 MB for a quality loss of less than 0.5%. To enable low-cost embedded DNNs, BioNetExplorer also employs different model compression techniques to further reduce the storage overhead of the network by up to 53\times for a quality loss of < 0.2\%.

Original languageEnglish (US)
Article number9377449
Pages (from-to)13251-13265
Number of pages15
JournalIEEE Internet of Things Journal
Issue number17
StatePublished - Sep 1 2021


  • Bio-signals
  • convolution
  • deep neural networks (DNNs)
  • efficiency
  • embedded systems
  • exploration
  • healthcare
  • long short-term memory (LSTM)
  • performance
  • wearables

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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