{"id":285,"date":"2023-02-08T11:47:19","date_gmt":"2023-02-08T11:47:19","guid":{"rendered":"https:\/\/icce.org\/2024\/tutorials\/"},"modified":"2024-01-05T09:16:18","modified_gmt":"2024-01-05T09:16:18","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/icce.org\/2024\/tutorials\/","title":{"rendered":"Tutorials"},"content":{"rendered":"\n \n
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Tutorials (5th Jan 2024)<\/h1>\n <\/div>\n <\/quillbot-extension>\n <\/quillbot-extension>\n <\/div>\n <\/section>\n
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Tutorials only are on 5th January 2024.<\/b>\n <\/h4>\n

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Please note that the time for lunch break is 12:30PM-1:30PM on January 5th ;
The ICCE 2024 conference does not provide free lunches for the participators of tutorials,
and the participators could find restaurants inside the hotel of the conference venue.\n <\/p>\n

Artificial Intelligence (AI) Track<\/h4>\n <\/div>\n <\/section>\n
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Time<\/th>\n Duration<\/th>\n Details<\/th>\n Speaker<\/th>\n <\/tr>\n <\/thead>\n
January 5th
8:30AM-
10:30AM\n <\/td>\n 		120 min<\/td>\n Tutorial A1:<\/b>\n Domain Adaptation at the Edge for Personalization of Deep Models<\/a>\n <\/td>\n Dr. Aswin Raghavan (SRI International)<\/td>\n <\/tr>\n
January 5th
10:30AM-
12:30PM\n <\/td>\n 		120 min<\/td>\n Tutorial A2:<\/b>\n Design and Deployment of Interoperable Deeply Quantized Neural Networks for Tiny Computing<\/a>\n <\/td>\n Danilo Pau (\u200b\u200bSTMicroelectronics)<\/td>\n <\/tr>\n
January 5th
1:30PM-
3:30PM\n <\/td>\n 		120 min<\/td>\n Tutorial A3:<\/b>\n Nets to Bytes in an Hour: Crafting and Deploying Optimal Tiny Deep Learning Networks with MATLAB and STM32Cube.AI Developer Cloud<\/a>\n <\/td>\n Brenda Zhuang (MathWorks), Danilo Pau (STMicroelectronics),
and Jack Ferrari (MathWorks)\n <\/td>\n <\/tr>\n
January 5th
3:30PM-
5:30PM\n <\/td>\n 		120 min<\/td>\n Tutorial A4:  \n Designing a safe autonomous vehicle computer - where we are, wherewe should be and a hands-on example<\/a>\n <\/span>\n <\/td>\n Prof. Dr. Milan Z. Bjelica
(Functional Safety Instructor @
UCSDX, CEO @ NIT Institute,
Associate Professor @ University
of Novi Sad)\n <\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n <\/div>\n <\/section>\n
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Alternate Track<\/h4>\n <\/quillbot-extension>\n <\/quillbot-extension-highlights>\n <\/div>\n <\/section>\n
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Time<\/th>\n Duration<\/th>\n Details<\/th>\n Speaker<\/th>\n <\/tr>\n <\/thead>\n
January 5th
8:30AM-
10:30AM\n <\/td>\n 		120 min<\/td>\n Tutorial B1:<\/b>\n Semantic Communication Concepts for Multimedia Applications in Emerging 6G Mobile Communication Systems<\/a>\n <\/td>\n Prof. Anil Fernando (University of Strathclyde)<\/td>\n <\/tr>\n
January 5th
10:30AM-
12:30PM\n <\/td>\n 		120 min<\/td>\n Tutorial B2:<\/b>\n Privacy and Security of Consumer Data in IoT Products<\/a>\n <\/td>\n Maria Rerecich (Consumer Reports)<\/td>\n <\/tr>\n
January 5th
1:30PM-
3:30PM\n <\/td>\n 		120 min<\/td>\n Tutorial B3:<\/b>\n 3rd Generation R&D<\/a>\n <\/td>\n Stu Lipoff (IP Action Partners Inc)<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n <\/div>\n <\/section>\n
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A1:  Domain Adaptation at the Edge for Personalization of Deep Models<\/b>\n <\/h3>\n
  120 minutes<\/h6>\n
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Dr. Aswin Raghavan<\/h6>\n

<\/i><\/i><\/i>SRI International\n <\/p>\n

Abstract<\/h4>\n

This\nhands-on tutorial will touch upon a few different aspects of training and\ndeploying deep learning-based models to edge or consumer devices. After\nreviewing basic and contemporary concepts such as self-supervised pre-training,\nwe will deploy a simple app. We will gradually cover concepts that enable us to\nadapt the model to the users\u2019 needs, making the model better, smaller, and\nfaster on the edge device.  Topics\ncovered will include Masked Modelling, Hyperdimensional Computing, and\nFederated Learning.  A basic ASIC\nunderstanding of machine learning terminology is useful but not required.<\/p>\n

Biography<\/h4>\n

Aswin\nRaghavan, PhD, is a Senior Computer Scientist at SRI International's Center for\nVision Technologies.  Aswin received his\nPhD in Computer Science from Oregon State University in 2017, during which he\nstudied probabilistic planning and deep learning. Since joining SRI, he has\ncontributed to the field of Edge Intelligence and created Deep Learning\nsolutions that can operate at the edge with low Size, Weight, and Power (SWaP),\nspanning topics such as quantization learning, data thinning, Hyperdimensional\nComputing, adversarial robustness, and few-shot domain adaptation at the edge.\nCurrently, he leads fundamental research projects as a Principal Investigator\n(PI) and leads multi-institution teams.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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A2:  Design and Deployment of Interoperable Deeply Quantized Neural Networks for Tiny Computing<\/b>\n <\/h3>\n
  120 minutes<\/h6>\n
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Danilo Pau<\/h6>\n

<\/i><\/i><\/i> STMicroelectronics
IEEE Fellow in \u200bComputer Society\n <\/p>\n \n