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Next-Gen Neuromorphic Researchers Look to Future

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Indhold leveret af EE Times On Air. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af EE Times On Air eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

In this special episode of the Brains and Machines podcast, Dr. Sunny Bains and Dr. Giulia D’Angelo talk to four early career researchers: Dr. Kenneth Stewart, a computer scientist at the U.S. Naval Research Laboratory in Washington DC; Dr. Laura Kriener, a postdoctoral researcher at The University of Bern in Switzerland; Jens Pedersen, a Ph.D. student at The Royal Institute of Technology (KTH) in Stockholm, Sweden; and Dr. Fabrizio Ottati, an AI/ML computer architect at NXP Semiconductors in Hamburg, Germany. They discuss learning rules for spiking neural networks, primitives for computations on neuromorphic hardware, and the benefits and drawbacks of neuromorphic engineering.

  continue reading

254 episoder

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Manage episode 421340625 series 2426529
Indhold leveret af EE Times On Air. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af EE Times On Air eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

In this special episode of the Brains and Machines podcast, Dr. Sunny Bains and Dr. Giulia D’Angelo talk to four early career researchers: Dr. Kenneth Stewart, a computer scientist at the U.S. Naval Research Laboratory in Washington DC; Dr. Laura Kriener, a postdoctoral researcher at The University of Bern in Switzerland; Jens Pedersen, a Ph.D. student at The Royal Institute of Technology (KTH) in Stockholm, Sweden; and Dr. Fabrizio Ottati, an AI/ML computer architect at NXP Semiconductors in Hamburg, Germany. They discuss learning rules for spiking neural networks, primitives for computations on neuromorphic hardware, and the benefits and drawbacks of neuromorphic engineering.

  continue reading

254 episoder

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