Algorithm-driven architectural design space exploration of domain-specific medical-sensor processors

Mohammed Shoaib, Niraj K. Jha, Naveen Verma

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Data-driven machine-learning techniques enable the modeling and interpretation of complex physiological signals. The energy consumption of these techniques, however, can be excessive, due to the complexity of the models required. In this paper, we study the tradeoffs and limitations imposed by the energy consumption of high-order detection models implemented in devices designed for intelligent biomedical sensing. Based on the flexibility and efficiency needs at various processing stages in data-driven biomedical algorithms, we explore options for hardware specialization through architectures based on custom instruction and coprocessor computations. We identify the limitations in the former, and propose a coprocessor-based platform that exploits parallelism in computation as well as voltage scaling to operate at a subthreshold minimum-energy point. We present results from post-layout simulation of cardiac arrhythmia detection with patient data from the MIT-BIH database. After wavelet-based feature extraction, which consumes 12.28 μJ, we demonstrate classification computations in the 12.00-120.05 μ J range using 10000-100000 support vectors. This represents 1170× lower energy than that of a low-power processor with custom instructions alone. After morphological feature extraction, which consumes 8.65 μJ of energy, the corresponding energy numbers are 10.24-24.51 μ J , which is 1548× smaller than one based on a custom-instruction design. Results correspond to V dd=0.4∼ V and a data precision of 8 b.

Original languageEnglish (US)
Article number6338362
Pages (from-to)1849-1862
Number of pages14
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number10
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Electrical and Electronic Engineering
  • Hardware and Architecture


  • Biomedical sensor processors
  • classification accelerators
  • embedded machine learning
  • low-energy design by voltage and precision scaling
  • structured hardware specialization
  • support-vector machines


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