title: Configurable analog-digital conversion using the neural engineering framework.
abstract: Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.
authors.affiliation: Neuromorphic Cognitive Systems Group, Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
authors.forename: Christian G
authors.initials: CG
authors.lastname: Mayr
authors.affiliation: Electrical Engineering and Information Science, Chair of Highly Parallel VLSI Systems and Neuromorphic Circuits, Technische Universität Dresden Dresden, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Partzsch
authors.affiliation: Electrical Engineering and Information Science, Chair of Highly Parallel VLSI Systems and Neuromorphic Circuits, Technische Universität Dresden Dresden, Germany.
authors.forename: Marko
authors.initials: M
authors.lastname: Noack
authors.affiliation: Electrical Engineering and Information Science, Chair of Highly Parallel VLSI Systems and Neuromorphic Circuits, Technische Universität Dresden Dresden, Germany.
authors.forename: Rene
authors.initials: R
authors.lastname: Schüffny
keywords: ADC with signal processing; multiple input ADC; neural engineering framework; neural network analog digital converter
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