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IIT-Guwahati develops new methods to solve computer systems domain’s problems

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GUWAHATI: The Indian Institute of Technology Guwahati (IIT-Guwahati) has developed new methods o solve the problems in the computer systems domain.

At a time when the world is rapidly moving towards research in applied areas, researchers from IIT-Guwahati have made fundamental contributions to memory architectures by preventing redundancy in data values and improving slow and frequent writes in the multi-core processor systems.

The research was conducted by Professor Hemangee K. Kapoor, Department of Computer Science
and Engineering (CSE), IIT Guwahati, comprising a team of research scholars: Sukarn
Agarwal, Palash Das, Sheel Sindhu Manohar, Arijit Nath, and Khushboo Rani.

Explaining the challenges of multi-core processor-based systems, Professor Hemangee K.Kapoor said, “The application data access patterns are not uniformly distributed and hence leads to several orders of writes to certain memory locations compared to others. Such heavily written locations become prone to wear-out and thus prevents the use of complete memory device without error corrections”.

To handle this non-uniformity, IIT Guwahati researchers developed methods to evenly distribute the accesses across the overall memory capacity to reduce the wear-out pressure on heavily written locations and also worked in the area which avoids writing redundant values thus prolonging the wear-out.

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“Slow and frequent writes can be re-directed to temporary SRAM partitions sparing the NVM from getting written with such frequent accesses. Such structures are called hybrid memories,” Kapoor added.

Kapoor further said “The team is also working on extending them to off-chip main memory. The future challenges
are to handle lifetime enhancement in presence of encryption methods used to secure the Non-
volatile memory and to handle temperature and process technology-driven disturbance errors
introduced when the cells are read or written.”

The researcher’s current and future contributions will help mitigate the drawbacks of promising
emerging memories and ease their adaptability.

Once some drawbacks are easily removed, scientists can find newer avenues for using such technologies without worrying about its
limitations.

Artificial Intelligence (AI) and Machine Learning (ML) are used as tools to solve several real-time problems. However, they involve enormous computations on huge datasets.

Building close to memory accelerators to process the data are efficient in performance as well as energy.

The research team is also working on building customized parallel architecture designs to give better
FLOPS.

Data creation is also fueled by 5G networks, image processing, and real-time voice processing. All these big-data applications need real-time analysis at run-time and with immediate responses.

Better storage and close to memory processing is the need of the hour. Non-volatile memory is advised to be used in the Internet of things (IoT) and edge devices.

The longevity of non-volatile memory in such devices is crucial for their service guarantees and durability.

Effective lifetime improvement methods will help to improve the state- of the art in this field which is still in its nascent stage. Solutions for better management of NVMs will give them wider acceptance in critical applications including healthcare and
autonomous vehicles.

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