EtaDac Lab
Emerging Technologies and Architectures for Data Communication Lab
OPL
Optical Processing Lab
Director
Dr. Somayyeh Koohi
PhD Students
Elham Khani (Co-Superviser)
Saeedeh Akbari (Biological Sequences Comparison Based on Optical Processing)
Hoda Sadeghzadeh (Design of optical convolutional nueral network for image classification)
Mahmood Kalemati (An efficient solution for drug target interaction and binding affinity prediction using deep learning methods )
Masoumeh Mehdinia
Kosar Yousefi-nezhad
MSc Students
Maryam Gheysari (Drug-target Interaction Prediction through Learning Methods for SARS-COV2 Based on Sequence and Structural Data)
Aida Ebrahimi (Designing an optical processing unit for non-Linear operations in deep neural networks)
Azam Abdosalehi (A Fast Alignment-free protein Comparison approach based on FPGA Implementation)
Farzin Mohammadi (DNA motif finding using edit-distance approach)
AmirHossein Mohammadi
(Developing a deep neural network for bio-sequence classification capable of
optical
computing)
Fatemeh Tabatabaee (prediction of DNA/RNA sequence binding site to protein with the ability to implement on GPU)
Mojtaba Zamani (Drug target binding affinity prediction using a deep generative model based on molecular and biological sequences)
Pouria Laghaee (Provide tools for clustering single cell data with low computational complexity)
Reyhaneh Ahmadi (Design of free-space optical spiking neural network)
Saeed Darvishi (MHC-Peptide binding prediction using a deep Learning method with efficient GPU implementation approach)
Graduated PhD Students
Melika Tinati (Advisor)
Graduated MSc Students
Roshanak Karimi
Negar Rezaei
Hossein Babashah
Ehsan Melaki
Ali Nezhadi
Ali-Reza Abolhasani
Alborz Derakhshan-far
Hamid-Reza Erfani
Saeedeh Akbari
Mahdieh Movahederad
Reza kalhor
Azhar Abbas
Research Projects
Bio-Photonics
Bioinformatics is an interdisciplinary field that expands methods and
tools for biological data comprehension, and benefits from several
sciences, such as computer science,
statistics, mathematics, and engineering to expound and analyze
biological data . As an important operation in
bioinformatics,
sequence alignment arranges the sequences of
DNA to
identify regions of similarity and differences between the sequences
to detect the genetic disease. Each DNA
sequence is represented with a string of A/G/T/C characters; each is
called a nucleotide, and composed of either of adenine (A), guanine (G),
thymine (T), or cytosine (C). As
the main purpose of sequence alignment algorithm, permanent change in
the
nucleotide sequence,
denoted as
mutations, should be located.
Mutations are classified either as
Small-scale mutations or as Large-scale mutations. A small scale
mutation which affect a small gene in one or a few nucleotides causes a
nucleotide base substitution, insertion, or deletion of the genetic
material, DNA
or RNA
. The mutations, which happen randomly, are
being recognized as particularly important in many genetic disorders.
Therefore, detecting and locating point mutations may be known as the
most important challenge for detecting genetic diseases.
The rapid growth of DNA sequence data in biology databases has
necessitated efficient collating,
organizing, identifying, retrieving, and searching the sequences
as DNA representation. It’s worse when a large amount of data
should be processed in real-time.
However, considering big-data
processing, traditional electronic computers suffer from many
limitations including high power consumption,
heat generation, high delay and
slow response .
To overcome electrical computer
limitations, optical computation has
been proposed . Capability of parallel processing in optical
computers motivates us to optically implement a global and local
sequence alignment method. For this purpose, optical correlation
benefits from the potential of high speed processing can
be employed to perform rapid global alignment for detecting high
similarity among various sequences.
Optical Supercomputers
The growing technologies have increased the demand of high performance computing. According to G. Moore’s low, number of transistor counts to be integrated per unit area in devices will almost double in one and half year. To achieve high speed computation, high packaging density in the logic circuits is required which results in more heat dissipation. The conventional computing is found unable to deal with low power, high compaction and heat dissipation issues of the current computing environment.
Optical Computing is computation with photon as opposed to conventional
electron based computation. Unmatched high speed and zero mass of photon
have attracted the researchers towards the optical realization of reversible
logic gates using Semiconductor Optical Amplifier (SOA) based Mach Zehnder
Interferometer (MZI) switches.
Emerging Technologies
for on-Chip Communication Architectures
The increasing interconnection bandwidth demands for chip multiprocessors
(CMP) cannot be simply satisfied by the reduction of the transistor feature
sizes and raising of the chip operation frequency. This problem stems from
many limitations associated with electrical interconnects, such as latency,
impedance, bandwidth, and power consumption.
Optical Interconnection Network for Data Centers
Data centers are experiencing an exponential increase in the amount of
network traffic that they have to sustain due to cloud computing and several
emerging web applications. To face this network load, large data centers are
required with thousands of servers interconnected with high bandwidth
switches. Current data center networks, based on electronic packet switches,
consume excessive power to handle the increased communication bandwidth of
emerging applications. Optical interconnects have gained attention recently
as a promising solution offering high throughput, low latency and reduced
energy consumption compared to current networks based on commodity switches.
Modeling of Optical
On-Chip Communication Architectures
Many of the modern Systems-on-Chip integrate a high density of
heterogeneous components such as different processors, a wide range of
hardware components, as well as complex interconnects that use different
communication protocols. On-chip physical interconnections represent a
limiting factor for the performance and energy consumption. Currently, the
optical interconnects integrated on chip are a viable alternative for on
chip interconnects. However, the access to physical prototyping of these
interconnects is a major challenge because this systems require very recent
technologies, still difficult to access. Thus, their high-level modeling and
validation are mandatory. The aim of this project is to propose a modeling
approach of the passive integrated photonic routing structures considering
physical-level delay and power issues.
Last Updated : January 2019