I am Shivani Shah, completed my Bachelor of Technology majoring in Information and Communication Technology from School of Engineering and Applied Science, Ahmedabad University. Currently, I am Research Scholar at IIIT Banglore. During my graduation, I have done a few projects such as 'Implementation of 8-bit MIPS processor on FPGA' (Coding Language: Verilog, Tool: Xilinx). I did my final year project on designing ‘Reduced Hardware Hybrid Branch Predictor’ at Ahmedabad University. Control Block is one of the major parts of any computer architecture, I focused on designing hybrid hardware, a combination of 1-bit predictor and 2-bit predictor, but hardware only of 2-bit predictor which drastically optimized the hardware requirements without compromising processor speed. So right now I have done instruction-level parallelism.

Aninda Manocha received her Ph.D student in Computer Science at Princeton University. She was advised by Professor Margaret Martonosi in the broad area of Computer Architecture. More specifically, she worked on the design of reconfigurable hardware to accelerate a variety of software applications with a focus on the application and compiler interface. She also worked on memory hierarchy designs and operating system techniques tailored to irregular, memory-intensive graph applications. She received her B.S. in Electrical and Computer Engineering as well as Computer Science from Duke University in 2018. She now works at Rivos.

I am a PhD Student at Chalmers University of Technology in Gothenburg, Sweden under the guidance of Professor Per Stenström. My research focuses on GPU architectures for approximate computing systems.

Charu Kalra is a PhD student in the Computer Engineering Department at Northeastern University. She is part of the Northeastern University Computer Architecture Research (NUCAR) group under the direction of Prof. David Kaeli. Her research interests include GPU compilers, software reliability, machine learning, workload characterization, and performance evaluation of GPU systems. Her PhD thesis focuses on design and evaluation of compiler-based techniques to predict and improve reliability of GPU applications. In 2014, Charu was featured on NVIDIA's 'Women Who CUDA' list. She has also pursued internships at AMD and AMD Research in the past.