The primary mission of triple-I T Bangalore
is essentially teaching research innovation.
This is a postgraduate-only institution,
so therein we have the Master of Technology programs,
Master of Science programs, and PhD programs.
We have a very thriving and active research group
in seven research domains, with data science, computer science,
software engineering, signal processing, networking
and communication, and IT in society
being some of those research domains.
Intel was making high-performance computing,
fully-configured environments on tap,
which allowed all our researchers to focus primarily
on problem solving, rather than on struggling
to get the infrastructure up and running.
So there have been workshops that
have been conducted for our students
to utilize the infrastructure that is being made available.
There has been constant support, so I
think Intel is the best support that we are getting,
in terms of taking our high-performance computing
onto the cloud.
Intel DevCloud uses a factor of two
or factor of three kind of advantage.
Being able to develop acoustic models also
lets us design the speech engines
for multiple Indian languages and English and so on.
They may use up to thousands [INAUDIBLE] of data,
and the cycle times for developing those algorithms
could be a week's time.
One of the significant advantages
we saw in Intel AI DevCloud is its optimized libraries,
including the Python environment, which allows
us to port the ASR platforms.
We routinely used it for quite some time,
including steps like feature extraction
from the signal, acoustic modeling, and speech decoding.
We also have plans to use the Intel Movidius Neural Compute
Stick.
Another application is basically the real-time streaming
demonstrator, which we want to build for speech recognition.
So we want to put a speech engine, which
runs on deep learning, onto the Compute Stick
and be able to show the real-time demonstrator
from streaming audio.
The second application is related
to more on the drone technology, where
we are looking at the on-edge device kind of applications
typically involving object recognition.
My thesis is on attention-based, end-to-end frameworks
for automatic speech recognition.
We are expecting to reduce model complexity
and improve our training and inference time,
and also gain deeper insights into speech recognition.
There was a workshop that was conducted
by Intel AI, which gave me more exposure to Intel AI DevCloud.
As a part of the student ambassador program,
we are trying to build a [INAUDIBLE] reduction
or keyword porting engine on the edge using the Raspberry
Pi and the hardware accelerator, which is Neural Compute Stick.
I have been working on speech-related technologies
for about 14 years.
Intel AI DevCloud gave me a performance boost
of two and a half times, compared
to our speaker verification system
and [INAUDIBLE] on desktop.
I was primarily looking at Intel Dev AI Cloud for its compute
and storage capacity.
This will help me perform the compute faster and also have
a storage because I had to write intermediary outputs, which
are very important for the next task in the scripts.
I am developing a low-power image inference
engine for inference of deep learning models.
I am also involved in developing UAVs
for assisted living ecosystems, especially
for elderly patients' care.
We are particularly interested in such applications wherein
visual stimulus will be used for navigation of the drone.
We are planning to use an Intel Movidius Neural Compute
Stick for the inferencing operation onboard the UAV.
We are also planning to use the same platform
for an agricultural drone, so we will
be developing deep-learning models, which
can detect any diseases for the plants,
especially by analyzing the color of the leaf and even
the soil pattern.
And then from that, it is possible to selectively spray
any solution that is required for the better
growth of the plant.
I am positive that with this kind of proactive support
that is coming from Intel and the constant engagement
that we have, we will get the most out
of this particular collaboration.
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