We describe an adaptive log domain filter with integrated learning rules for model reference estimation. The system is a first-order low pass filter implemented using multiple input floating gate transistors operating in subthreshold to realize on-line learning of gain and cut-off frequency. We use adaptive dynamical system theory to derive robust control laws for gain and cut-off frequency adaptation in a system identification task. Simulation results show that convergence is slower using simplified control laws but still occurs within milliseconds. Experimental results confirm that the estimated gain and cut-off frequency track the parameters of the reference filter. The adaptive log domain filter has measured power consumption of 33 lW. During operation, deterministic errors are introduced by mismatch within the analog circuit implementation. An analysis is presented which attributes the errors to current mirror mismatch.
We present an adaptive log domain filter with integrated learning rules for model reference estimation. The system is a first order low pass filter based on a log domain topology that incorporates multiple input floating gate transistors to implement on-line learning of gain and time constant. Adaptive dynamical system theory is used to derive robust learning rules for both gain and timeconstant adaptation in a system identification task. The adaptive log domain filters have simulated cutoff frequencies above 100kHz with power consumption of 23 W and show robust adaptation of the estimated gain and time constant as the parameters of the reference filter are changed.
-People rarely put in their papers the things that didn't work, the mistakes they made, and how they found out what went wrong. Such confessions can help others learn how to avoid similar mistakes. Twenty-six confessions were collected to form the bulk of this paper. Themes that arise are errors that result from not understanding the limitations of simulation tools in modeling physical reality, chip verification errors that result from lack of clear communication between designers, and projects that are considered in their own isolated environment of technical challenges rather than the broader context of their environment or application.
We describe an adaptive log domain second order filter with integrated learning rules. The system is implemented using multiple input floating gate transistors to realize on-line learning of quality factor and time constant. We use adaptive dynamical system theory to derive robust control laws for both quality factor and time constant adaptation in a system identification task. The log domain filters adapt to estimate the quality factor and time constant of a reference filter accurately and efficiently as the parameters of the reference are changed. We present simulation results for 0.5µm technology which demonstrate that adaptation occurs within milliseconds. The filter consumes 2µW and the entire system consumes 10µW.I.
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