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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260107T101115Z
UID:B7ED5F51-169A-44D1-9024-823E90956204
DTSTART;TZID=Asia/Kolkata:20251012T090000
DTEND;TZID=Asia/Kolkata:20251012T160000
DESCRIPTION:The AI on the Edge workshop provided participants with a compre
 hensive understanding of how Artificial Intelligence can be deployed on ed
 ge devices to enable fast\, efficient\, and scalable real-time processing.
  The session highlighted the significance of bringing AI computation close
 r to the data source and demonstrated practical techniques used in modern 
 embedded and IoT-based systems.\n\nThe workshop covered:\n\n1. The worksho
 p introduced the fundamentals of Edge AI\, highlighting its advantages ove
 r cloud-based models and showcasing real-world applications in smart devic
 es\, robotics\, healthcare\, and Industry 4.0.\n\n2. Common edge hardware 
 platforms such as Raspberry Pi\, NVIDIA Jetson\, Google Coral TPUs\, and m
 icrocontroller-based ML boards were discussed\, with emphasis on their cap
 abilities and limitations.\n\n3. Model-optimization techniques—including
  quantization\, pruning\, and converting models into lightweight formats l
 ike TensorFlow Lite and ONNX—were explained for efficient deployment on 
 low-power devices.\n\n4. Hands-on demonstrations allowed students to work 
 with TinyML and edge-deployment tools\, practicing inference execution\, s
 ensor data handling\, and power-efficient AI design.\n\n5. Practical appli
 cations such as real-time object detection\, keyword spotting\, anomaly de
 tection\, and sensor-driven AI solutions were showcased.\n\n6. Key conside
 rations related to security\, latency\, speed\, memory\, and power consump
 tion were discussed to help attendees understand the trade-offs in Edge AI
  system design.\n\nPSG COLLEGE OF TECHNOLOGY\, PEELAMEDU\, COIMBATORE\, Ta
 mil Nadu\, India\, 641004
LOCATION:PSG COLLEGE OF TECHNOLOGY\, PEELAMEDU\, COIMBATORE\, Tamil Nadu\, 
 India\, 641004
ORGANIZER:ieeesc12951@psgtech.ac.in
SEQUENCE:4
SUMMARY:AI ON THE EDGE
URL;VALUE=URI:https://events.vtools.ieee.org/m/527245
X-ALT-DESC:Description: &lt;br /&gt;&lt;div class=&quot;WordSection1&quot;&gt;\n&lt;p class=&quot;MsoBody
 Text&quot; style=&quot;text-align: justify\; line-height: 115%\; margin: 10.55pt 36.
 7pt .0001pt 36.6pt\;&quot;&gt;&lt;span lang=&quot;EN-US&quot;&gt;The &lt;em style=&quot;mso-bidi-font-styl
 e: normal\;&quot;&gt;AI on the Edge &lt;/em&gt;workshop provided participants with a com
 prehensive understanding of how Artificial Intelligence can be deployed on
  edge devices to enable fast\, efficient\, and scalable real-time processi
 ng. The session highlighted the significance of bringing AI computation cl
 oser to the data source and demonstrated practical techniques used in mode
 rn embedded and IoT-based &lt;span style=&quot;letter-spacing: -.1pt\;&quot;&gt;systems.&lt;/
 span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoBodyText&quot;&gt;&lt;span lang=&quot;EN-US&quot;&gt;&amp;nbsp\; &amp;nbsp\
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  lang=&quot;EN-US&quot; style=&quot;font-size: 14.0pt\; mso-bidi-font-size: 11.0pt\; line
 -height: 115%\;&quot;&gt;The workshop introduced the fundamentals of Edge AI\, hig
 hlighting its advantages over cloud-based models and showcasing real-world
  applications in smart devices\, robotics\, healthcare\, and Industry 4.0.
 &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoListParagraph&quot; style=&quot;text-align: justify\; line
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 agraph&quot; style=&quot;text-align: justify\; line-height: 115%\; mso-list: l0 leve
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 ont-size: 11.0pt\; line-height: 115%\;&quot;&gt;Model-optimization techniques&amp;mdas
 h\;including quantization\, pruning\, and converting models into lightweig
 ht formats like TensorFlow Lite and ONNX&amp;mdash\;were explained for efficie
 nt deployment on low-power devices.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoListParagraph
 &quot; style=&quot;text-align: justify\; line-height: 115%\; mso-list: l0 level1 lfo
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 &lt;!--[endif]--&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 14.0pt\; mso-bidi-font-
 size: 11.0pt\; line-height: 115%\;&quot;&gt;Hands-on demonstrations allowed studen
 ts to work with TinyML and edge-deployment tools\, practicing inference ex
 ecution\, sensor data handling\, and power-efficient AI design.&lt;/span&gt;&lt;/p&gt;
 \n&lt;p class=&quot;MsoListParagraph&quot; style=&quot;text-align: justify\; line-height: 11
 5%\; mso-list: l0 level1 lfo1\; tab-stops: 72.6pt\; margin: 7.9pt 36.65pt 
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 &quot;font-size: 14.0pt\; mso-bidi-font-size: 11.0pt\; line-height: 115%\; font
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 &quot;&gt;&amp;nbsp\;&lt;/p&gt;
END:VEVENT
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