Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC

Zhuo Wang, Jintao Zhang, Naveen Verma

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

In wearable and implantable medical-sensor applications, low-energy classification systems are of importance for deriving high-quality inferences locally within the device. Given that sensor instrumentation is typically followed by A-D conversion, this paper presents a system implementation wherein the majority of the computations required for classification are implemented within the ADC. To achieve this, first an algorithmic formulation is presented that combines linear feature extraction and classification into a single matrix transformation. Second, a matrix-multiplying ADC (MMADC) is presented that enables multiplication between an analog input sample and a digital multiplier, with negligible additional energy beyond that required for A-D conversion. Two systems mapped to the MMADC are demonstrated: (1) an ECG-based cardiac arrhythmia detector; and (2) an image-pixel-based facial gender detector. The RMS error over all multiplication performed, normalized to the RMS of ideal multiplication results is 0.018. Further, compared to idealized versions of conventional systems, the energy savings obtained are estimated to be 13 × and 29 ×, respectively, while achieving similar level of performance.

Original languageEnglish (US)
Article number7366769
Pages (from-to)825-837
Number of pages13
JournalIEEE transactions on biomedical circuits and systems
Volume9
Issue number6
DOIs
StatePublished - Dec 2015

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Electrical and Electronic Engineering

Keywords

  • ADC
  • Embedded sensing
  • boosting
  • classification

Fingerprint Dive into the research topics of 'Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC'. Together they form a unique fingerprint.

  • Cite this