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.
Training Probabilistic VLSI models On-chip to Recognise Biomedical Signals under Hardware Nonidealities. Publishing Authors By Initials
Training Probabilistic VLSI models On-chip to Recognise Biomedical Signals under Hardware Nonidealities. Journal Published:
PUBLICATION TYPE: Journal Article
Journal: Conference proceedings : ... Annual International
VOLUME: 1
Page Numbers: 5354-7
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ISSN: 1557-170X
DAY: 23
MONTH: 10
YEAR: 2006
Training Probabilistic VLSI models On-chip to Recognise Biomedical Signals under Hardware Nonidealities. Information
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LANGUAGE: eng
NlmUniqueID: 101243413
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Grant and Affiliation Information for Training Probabilistic VLSI models On-chip to Recognise Biomedical Signals under Hardware Nonidealities.
AFFILIATION: Inst. of Electron. Eng., Nat. Tsing Hua Univ., Hsinchu.
Country: United States
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MEDLINETA: Conf Proc IEEE Eng Med Biol So
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