I am a PhD candidate in the Computer Science Department at Georgia Institute of Technology advised by Professor Hadi Esmaeilzadeh. I received my Bachelors (2012) in Electrical Engineering from Indian Institute of Technology Ropar, India where I was honored with the President of India Gold medal for my outstanding academic performance. Subsequently, I completed my Masters (2014) from the University of Texas, at Austin in Electrical and Computer Engineering. I joined my PhD studies in Fall 2014 and since have been a part of Alternate Computing Technologies lab. My research interests include computer architecture, microarchitecture design, and developing alternative technologies for efficient computing. I am continuously working towards designing full stack solutions and template-based architectures for accelerating Machine Learning and Deep Learning algorithms on an FPGA. Besides my primary research-area of computer architecture, I have also worked at the intersection of machine learning, hardware design, programming languages and databases. In my free time, I like to spend time oil painting, cooking, and reading novels.

I am a PhD candidate in the Department of Computer Science & Engineering at the University of California, Riverside. I work with Distinguished Prof. Laxmi Narayan Bhuyan and Prof.Daniel Wong and am a member of SoCal Lab at UCR. My interest lies in Computer Architecture , GPGPU Architecture design, High Performance Computing, Fault-Tolerance systems. I have worked on multiple projects on Data- Dependent Applications on GPGPU, Low power Design of GPGPU Execution units and have achieved notable improvements in terms of Performance gain and Power and Area saving.

Thesis Title: LongLiveNoC: Wear Levelling, Write Reduction and Selective VC allocation for Long lasting Dark Silicon aware NoC Interconnects

Increasing processing demand has led to the development of chip multiprocessors which can have multiple to many cores connected with each other and with the on-chip caches. These connections are established by an on-chip packet-switched Network-on-Chip (NoC). Scaling of technology nodes increases the power dissipated by the chips leading to thermal restrictions. To control the chip thermal design power, certain components (like cores and caches) may be turned off. However, in this scenario of dark silicon, the interconnect is expected to be available.

The thesis aims to save power consumed by this always ON interconnect by replacing the power-hungry SRAM buffers in the routers with low leakage Non-Volatile Memory (NVM) based buffers. However, the major challenges with the employment of the NVMs are slower writes and weak write endurance.

The thesis proposes:
1. Methods to evenly distribute the writes across these NVM buffers in order to increase their lifetime. This is done by static and dynamic allocation of buffers to the virtual channels and their selection during packet transmission.

2. Power can also be saved by using frequency scaling of the routers and/or turning off certain buffers when the usage is less. The investigation is done for all such approaches and power savings are demonstrated.

3. Endurance can be improved and energy can be saved if we can reduce the number of writes performed on the buffers. This is achieved by proposing two compression techniques leading to reduced network traffic and improved lifetime.

All the above methods help in improving the lifetime of the NVM based NoC interconnects in the context of dark silicon.