TY - JOUR
T1 - Parametric fault identification and dynamic compensation techniques for cellular neural network hardware
AU - Grimaila, M.R.
AU - Pineda de Gyvez, J.
PY - 1996
Y1 - 1996
N2 - Testing strategies to quantify parametric faults in a fully programmable, two-dimensional cellular neural network (CNN) are presented. The approach is intended to quantify system offsets, time constant mismatches, nonlinearities in the multipliers and state nodes, and the magnitude of the dynamic range of operation which can lead to misconvergence in the CNN array. For some cases, the authors present dynamic solutions by compensating the templates, the input data, and/or the initial condition values to minimise or cancel the undesired effects. The proposed dynamic compensation techniques can be applied to any CNN independent of the array size or topology. To demonstrate the feasibility of the proposed techniques, the authors examine their application to an actual complex VLSI CNN implementation
AB - Testing strategies to quantify parametric faults in a fully programmable, two-dimensional cellular neural network (CNN) are presented. The approach is intended to quantify system offsets, time constant mismatches, nonlinearities in the multipliers and state nodes, and the magnitude of the dynamic range of operation which can lead to misconvergence in the CNN array. For some cases, the authors present dynamic solutions by compensating the templates, the input data, and/or the initial condition values to minimise or cancel the undesired effects. The proposed dynamic compensation techniques can be applied to any CNN independent of the array size or topology. To demonstrate the feasibility of the proposed techniques, the authors examine their application to an actual complex VLSI CNN implementation
U2 - 10.1049/ip-cds:19960478
DO - 10.1049/ip-cds:19960478
M3 - Article
SN - 1350-2409
VL - 143
SP - 295
EP - 301
JO - IEE Proceedings - Circuits, Devices and Systems
JF - IEE Proceedings - Circuits, Devices and Systems
IS - 5
ER -