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An Associative Memory with oscillatory CNN arrays using spin torque oscillator cells and spin-wave interactions architecture and End-to-end Simulator

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8 Author(s)
Roska, T. ; Pazmany Univ., Budapest, Hungary ; Horvath, A. ; Stubendek, A. ; Corinto, F.
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An Associative Memory is built by three consecutive components: (1) a CMOS preprocessing unit generating input feature vectors from picture inputs, (2) an AM cluster generating signature outputs composed of spintronic oscillator (STO) cells and local spin-wave interactions, as an oscillatory CNN (O-CNN) array unit, applied several times arranged in space, and (3) a classification unit (CMOS). The end to end design of the preprocessing unit, the interacting O-CNN arrays, and the classification unit is embedded in a learning and optimization procedure where the geometric distances between the STOs in the O-CNN arrays play a crucial role. The O-CNN array has an input vector as a 1D array of oscillator frequencies, and the synchronized O-CNN array codes the output as the phases of the output 1D array. The typical O-CNN array has 1-3 rows of STOs. Simplified STO and interaction macro models are used. A typical example is shown using an End-to-end Simulator.

Published in:

Cellular Nanoscale Networks and Their Applications (CNNA), 2012 13th International Workshop on

Date of Conference:

29-31 Aug. 2012