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Department of Electronics and Communication Engineering, Nirma University

What does Machine Learning have for an EC Engineer?

Being an EC Engineer, which are the areas where I can employ machine
learning (ML)/ Deep learning (DL)? This could be the question that would be

bugging a lot of students like you, these days. The field of ML and DL is not
just limited to computer/ IT applications like face recognition, object detection,
or prediction of weather and stock market. You can actually apply this buzz
word and booming technology in applications related to Electronics and
Communication – your own degree of Engineering. For instance, let’s talk about:
Dr Ruchi Gajjar
 VLSI design, then ML is currently used in Chip Design (e.g., new Assistant Professor, EC
interconnect fabrics, new combinations of memory and computation, etc.),
predicting places where chip may experience manufacturing defects, load prediction on CPU, voltage scaling
to save energy. For an ASIC design, ML can be applied for RTL code analysis to detect and correct problems
for scan insertion or for coding guideline violations, Regression analysis in Verification for identifying test

cases, in Synthesis for early detection of issues with floor planning or congestion early, before and after the
layout.
 Electronics, where ML is used for prediction of successful field-programmable gate arrays (FPGA) compilation
strategies, behavioral modeling of microelectronic circuits and systems, to predict the
Power/performance/area (PPA) given a register-transfer level description of a circuit, eliminating the need to
undertake the lengthy physical design process.
 Antenna and Wireless Communication, where ML is used for parameter optimization in antenna design and
Wireless Communication offers a wide scope for ML in areas like channel modelling, signal estimation, and

detection, energy efficiency, cognitive radios, wireless sensor networks, vehicular communications, and
wireless multimedia communications. To give you a better idea, ML is used for resource management like
power control, spectrum management, backhaul management, cache management, and beamformer design and
computation resource management in the MAC layer, networking and mobility management in the network
layer for applications in clustering, base station switching control, user association, and routing, and
localization in the application layer

And trust me; this is just the tip of the iceberg. If you dig in a little further, you may find that ML has
applications in almost every course that you have studied/ are studying.

So, there’s no need for you to leave your core branch in the race of doing something in ML, but rather you can
come out with project and research with is an amalgamation of EC and ML. You just need to put on your
thinking hats, use your domain expertise, and of course, a little bit of ML, and who knows, you may come out
with solutions to conventional problems listed above and many more.

Good luck and happy learning…! Waiting to see your accomplishments.

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