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.

An Associative Memory with oscillatory CNN arrays using spin torque oscillator cells and spin-wave interactions architecture and End-to-end Simulator / Roska, T.; Horvath, A.; Stubendek, A.; Corinto, Fernando; Csaba, G.; Porod, W.; Shibata, T.; Bourianoff, G.. - (2012), pp. 1-3. (Intervento presentato al convegno CNNA 2012 - 13th International Workshop on Cellular Nanoscale Networks and their Applications tenutosi a Torino nel 28-31/08/2012) [10.1109/CNNA.2012.6331463].

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

CORINTO, FERNANDO;
2012

Abstract

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.
2012
9781467302876
9781467302883
9781467302890
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2504314
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