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Fri, 6 May 2022 16:49:23 +0200
Carlo Vallati <[log in to unmask]>
ACM SIGMM Interest List <[log in to unmask]>
Carlo Vallati <[log in to unmask]>
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Dear all,

we are looking for bright and highly motivated student for one PhD 
position at the Department of Information Engineering at the University 
of Pisa.

The position is funded within the framework of the "Crosslab: Innovation 
for Industry 4.0" project.
The research activities will be carried out in the "Cloud Computing, Big 
Data & Cybersecurity" laboratory 

A short description of the research topic can be found below.

Interested people are requested to send an expression of interest by 
submitting a curriculum vitae, a one-page research statement showing 
motivation and understanding of the topic of the position, and the 
official Transcript of Record. The expression of interest must be sent 
by email to Carlo Vallati at [log in to unmask] with the reference 
[PhD expression of interest] in the subject of the email. Applications 
will be reviewed continuously until 5th July 2022.

The starting date of the PhD position is Fall 2022. The duration of the 
PhD is three years. The compensation is a standard Italian Ph.D. student 
fare, about 1150 Euro/month net.

Edge Computing 2.0: Efficient Deep Learning at the Edge

Abstract: Deep neural networks (DNNs) have achieved unprecedented 
success in the field of artificial intelligence (AI), including computer 
vision, natural language processing, and speech recognition. However, 
their superior performance comes at the considerable cost of 
computational complexity, which greatly hinders their applications in 
many resource-constrained devices, such as Edge computing nodes and 
Internet of Things (IoT) devices. Therefore, methods and techniques that 
can lift the efficiency bottleneck while preserving the high accuracy of 
DNNs are in great demand to enable numerous edge AI applications.

The proposed research plan involves the analysis and identification of 
the challenges related to DNNs for time series prediction both at 
training time, on the GPU-enabled resource-constrained devices, and at 
inference time, on microcontrollers, leveraging available open-source 
software such as Tensorflow and Pytorch. The final goal of the research 
activity will be the definition, design, implementation, and testing of 
novel algorithms to improve the efficiency of DNNs on the edge and on
IoT devices, on real-case scenarios.

Reference contact: Carlo Vallati, email: [log in to unmask]




Carlo Vallati, PhD
Associate Professor
Computer Networking Group
Department of Information Engineering
University of Pisa
Via Diotisalvi 2, 56122 Pisa - Italy
Ph. : (+39) 050-2217.572 (direct) .599 (switch)
Fax : (+39) 050-2217.600
Skype: warner83
E-mail:[log in to unmask]




[log in to unmask]

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