Training probabilistic VLSI models on-chip to recognise biomedical signals under hardware nonidealities

P.C. Jiang; H. Chen

doi:10.1109/IEMBS.2006.260401

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Conference paper

Training probabilistic VLSI models on-chip to recognise biomedical signals under hardware nonidealities

P.C. Jiang and H. Chen

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp.5354-5357

2006

DOI: https://doi.org/10.1109/IEMBS.2006.260401

Abstract

VLSI implementation of probabilistic models is attractive for many biomedical applications. However, hardware non-idealities can prevent probabilistic VLSI models from modelling data optimally through on-chip learning. This paper investigates the maximum computational errors that a probabilistic VLSI model can tolerate when modelling real biomedical data. VLSI circuits capable of achieving the required precision are also proposed. © 2006 IEEE.

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Title: Training probabilistic VLSI models on-chip to recognise biomedical signals under hardware nonidealities
Creators - without role: P.C. Jiang - National Tsing Hua University
H. Chen - National Tsing Hua University
Identifiers: 1424400325; 9781424400324; 9957772800306774
Academic Unit: Institute of Electronics Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University
Language: English
Resource Type: Conference paper
Publication Details: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp.5354-5357

Training probabilistic VLSI models on-chip to recognise biomedical signals under hardware nonidealities

Abstract

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