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DTSTART:20251026T020000
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DTSTAMP:20260319T083051Z
UID:D311CD79-F93C-435F-B3F2-79EDA05596A7
DTSTART;TZID=Europe/Zurich:20260313T160000
DTEND;TZID=Europe/Zurich:20260313T170000
DESCRIPTION:Distinguished Lecture by Jason K. Eshraghian\n\nNeuromorphic La
 rge Language Models (LLMs)\n\nAbstract: The brain is the perfect place to 
 look for inspiration to develop more efficient neural networks. Inspired b
 y the recurrent dynamics of biological neurons\, this talk will present se
 veral frontier reasoning LLMs developed in my lab\, from software to devic
 e deployments. Trained end-to-end in an academic lab on a full production 
 pipeline (data curation\, pre-training\, to post-training and alignment) t
 hese models surpass all leading LLMs from Meta\, Google and every other ov
 er-resourced company in the ~10-billion parameter regime\, despite being ~
 5x smaller. We have deployed several of our models on neuromorphic hardwar
 e at 2-watts\, bringing SoTA-level reasoning from the datacenter to the ed
 ge.\n\nThe lecture will be hosted by the Institute for NeuroInformatics (I
 NI) at the University of Zurich\, as part of the Spring 2026 colloquia ser
 ies.\n\nRegistration is free but mandatory.\n\nCo-sponsored by: Institute 
 of Neuroinformatics\, University of Zürich\n\nSpeaker(s): \, Jason K. Esh
 raghian \n\nRoom: F51\, Bldg: Y35\, University of Zürich\, Irchel Campus\
 , Institute of Neuroinformatics\, Zurich\, Switzerland\, Switzerland
LOCATION:Room: F51\, Bldg: Y35\, University of Zürich\, Irchel Campus\, In
 stitute of Neuroinformatics\, Zurich\, Switzerland\, Switzerland
ORGANIZER:lbegon@ethz.ch
SEQUENCE:10
SUMMARY:CAS Distinguished Lecture - J. K. Eshraghian UC Santa Cruz - Neurom
 orphic LLMs
URL;VALUE=URI:https://events.vtools.ieee.org/m/533853
X-ALT-DESC:Description: &lt;br /&gt;&lt;p style=&quot;text-align: center\;&quot;&gt;&lt;span class=&quot;
 mark0p63h6u9h&quot; data-markjs=&quot;true&quot; data-ogac=&quot;&quot; data-ogab=&quot;&quot; data-ogsc=&quot;&quot; d
 ata-ogsb=&quot;&quot; data-olk-copy-source=&quot;MessageBody&quot;&gt;Distinguished Lecture by Ja
 son K. Eshraghian&lt;/span&gt;&lt;/p&gt;\n&lt;p style=&quot;text-align: center\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\
 n&lt;p style=&quot;text-align: center\;&quot;&gt;&lt;strong&gt;&lt;span class=&quot;mark0p63h6u9h&quot; data-
 markjs=&quot;true&quot; data-ogac=&quot;&quot; data-ogab=&quot;&quot; data-ogsc=&quot;&quot; data-ogsb=&quot;&quot; data-olk
 -copy-source=&quot;MessageBody&quot;&gt;&lt;span data-olk-copy-source=&quot;MessageBody&quot;&gt;Neurom
 orphic Large Language Models (LLMs)&lt;/span&gt;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;
 text-align: center\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;span class=&quot;mark0p63h6u9h&quot; 
 data-markjs=&quot;true&quot; data-ogac=&quot;&quot; data-ogab=&quot;&quot; data-ogsc=&quot;&quot; data-ogsb=&quot;&quot; dat
 a-olk-copy-source=&quot;MessageBody&quot;&gt;Abstract&lt;/span&gt;: &lt;/strong&gt;The brain is the
  perfect place to look for inspiration to develop more efficient neural ne
 tworks. Inspired by the recurrent dynamics of biological neurons\, this ta
 lk will present several frontier reasoning LLMs developed in my lab\, from
  software to device deployments. Trained end-to-end in an academic lab on 
 a full production pipeline (data curation\, pre-training\, to post-trainin
 g and alignment) these models surpass all leading LLMs from Meta\, Google 
 and every other over-resourced company in the ~10-billion parameter regime
 \, despite being ~5x smaller. We have deployed several of our models on ne
 uromorphic hardware at 2-watts\, bringing SoTA-level reasoning from the da
 tacenter to the edge.&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;&lt;em&gt;The lecture will 
 be hosted by the Institute for NeuroInformatics (INI) at the University of
  Zurich\, as part of the Spring 2026 colloquia series. &lt;/em&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp
 \;&lt;/p&gt;\n&lt;p&gt;&lt;em&gt;Registration is free but mandatory.&lt;/em&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p
 &gt;
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