Training probabilistic VLSI models on-chip to recognise biomedical signals under hardware nonidealities.
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.
Jiang
P C
PC
Institute of Electronics Engineering, National Tsing-Hua University, HsinChu, Taiwan. g935011@oz.nthu.edu.tw
Chen
H
H
D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering / Q000379:methods*; D003201:Computers; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000473:pathology; D016011:Normal Distribution; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D012984:Software