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I am a Ph.D. candidate in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD). I received my M.S. and B.Sc degrees from the School of Electrical and Computer Engineering (ECE) at the University of Tehran (UT), Tehran in 2014 and 2011 respectively.
I am a member of System Energy Efficiency Lab (SeeLab) working with Professor Tajana Rosing. My research interests are brain-inspired computing, computer architecture, and machine learning. Based on the research that I have started, we opened a new sub-group in SeeLab (Emerging Computing), where I am closely mentoring more than 20 graduate and undergraduate students (more info here). I have published over 70 papers at top-tier conferences and journals including HPCA, DAC, DATE, FPGA, ICCAD, TCAD. I have also gotten several awards during my study including nomination for the Bernard and Sophia Gorden Engineering leadership award (2019), the best doctoral award for excellence in research (2018), nomination for outstanding graduate student award, Jacobs school of engineering Powell Fellowship (2014-2017), and the best paper candidate awards at ISQED 2018 and ICCD 2016 conferences.
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Research Projects
Brain-Inspired Hyperdimensional Computing:
Although current computers are significantly powerful performing arithmetic operations, they show very low efficiency when processing the cognitive tasks. In contract, human brain's are surprisingly efficient and fast in processing the cognitive tasks, even though they are feeble in calculating with numbers. This fact motivates us to design new algorithms which can have similar functionality as brain. The mathematical properties of high-dimensional spaces show remarkable agreement with behaviors controlled by the brain. Brain-inspired hyperdimensional (HD) computing explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. HD computing provides an opportunity for robust, general and scalable model of computing using hypervectors in thousand dimensions.
Although current computers are significantly powerful performing arithmetic operations, they show very low efficiency when processing the cognitive tasks. In contract, human brain's are surprisingly efficient and fast in processing the cognitive tasks, even though they are feeble in calculating with numbers. This fact motivates us to design new algorithms which can have similar functionality as brain. The mathematical properties of high-dimensional spaces show remarkable agreement with behaviors controlled by the brain. Brain-inspired hyperdimensional (HD) computing explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. HD computing provides an opportunity for robust, general and scalable model of computing using hypervectors in thousand dimensions.
In-Memory Accelerators:
Many machine learning and cognitive applications process large data set collected in the Internet of Things domain. Running the data-intensive workloads on traditional cores results in high energy consumption and slow processing speed. This inefficiency stems from the large amount of data movements between memory and processing units in the traditional architectures. In addition, many cognitive systems, e.g., big data analysis and healthcare applications, assimilate the data using deep neural networks and classification algorithms which are also computationally expensive in the traditional Von Neumann architecture. We design novel memory architectures which completely replaces the traditional processing units and performs brain-inspired computations such as deep neural networks and hyperdimensional computing.
Many machine learning and cognitive applications process large data set collected in the Internet of Things domain. Running the data-intensive workloads on traditional cores results in high energy consumption and slow processing speed. This inefficiency stems from the large amount of data movements between memory and processing units in the traditional architectures. In addition, many cognitive systems, e.g., big data analysis and healthcare applications, assimilate the data using deep neural networks and classification algorithms which are also computationally expensive in the traditional Von Neumann architecture. We design novel memory architectures which completely replaces the traditional processing units and performs brain-inspired computations such as deep neural networks and hyperdimensional computing.
Approximate Computing:
We propose a novel architecture with software and hardware support for approximate computing. Hardware components are enhanced with ability to dynamically adapt approximation at a quantifiable and controllable cost in terms of accuracy. Software services complement hardware to ensure user’s perception is not compromised, while maximizing the energy savings due to approximations. The changes to hardware design include approximation enabled CPU, GPU, accelerators (DSPs) and storage.
We propose a novel architecture with software and hardware support for approximate computing. Hardware components are enhanced with ability to dynamically adapt approximation at a quantifiable and controllable cost in terms of accuracy. Software services complement hardware to ensure user’s perception is not compromised, while maximizing the energy savings due to approximations. The changes to hardware design include approximation enabled CPU, GPU, accelerators (DSPs) and storage.
Mentorship:
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I am closely mentoring about 20 graduate and undergrad students including four PhD students. I always consider diversity and minority seriously during my mentorship. I was honored to mentor about 14 female students toward their graduate and undergraduate research. Based on their research, we have published more than 7 papers in the top venue conferences/journals. I have also volunteered to participated in ESRP (2018) and ENLACE (2017, 2018) programs which aim to support undergraduate and Latino research at UC San Diego. More info can be found here.
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Grants and Funding Involvement
I have actively contributed on on several grants funded at UC San Diego:
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JUMP CRISP: Center for Research on Intelligent Storage and Processing-in-memory ($30M Grants)
SRC Grant: IoT System Reliability and Maintaiinability ($400,000 Grants) |
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NSF grants #1527034: ENACT: Environment-Aware Management of Mobile Systems
(Awarded Amount: $466,000) NSF CHASE-CI #1730158: Cognitive Hardware and Software Ecosystem Community Intrastructure (Awarded Amount: $1,199,998) |
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What's New?
New! Dec. 2018. Accepted papers in IEEE TC, ACM JETC, and IEEE D&T journals! New! Dec. 2018. Being nominated for the Bernard and Sophia Gordon Engineering Leadership Award! New! Dec. 2018. Accepted for DATE 2019 PhD Forum! New! Nov. 2018. Accepted paper in FPGA'19! Oct. 2018. Three accepted papers in DATE'19! Sept. 2018. Three accepted papers in ASPDAC'19! June 2018. Accepted papers in ICCAD'18! June 2018. Doctoral Award for Excellence in Research (link)! April 2018. Best poster award ResearchExpo'18! Presenter Daniel Peroni. April 2018. Presentation at NVMW'18 (La Jolla), ISQED'18 (Santa Clara) and BHI'18 (Las Vegas)! April 2018. Invitation talk at UC Berkeley, Thanks Prof. Rabeay for hosting. March 2018. Best paper candidate ISQED'18! Thanks Joonseop for the great presentation! Jan 2018. Program committee member in 14th IEEE PRIM'18 and 31st IEEE SOCC'18 Jan 2018. Invitation talk from CSES at CSE day, UCSD! Nov-Oct 2017. Presentation at CNS'17 (La Jolla), ICRC (Washington Dc), ICCAD (Irvine) Sep 2017. IEEE invitation talk at San Diego State University, CA. Sep 2017. Teaching two undergraduate courses at UC San Diego, check here! June-Sept. 2017. Paper presentation at DAC (Austin), Techcon (Austin)! June 2017. My mentee Max Masich defensed his master thesis! June 2017. My mentee Deqian Kong selected as best undergrad researcher at CSE [Picture]! May 2017. Member of program committee in IEEE SOC'17 and IEEE PRIM'17! March 2017. Paper presentation at NVMW (La Jolla), ISQED (Santa Clara)! Aug 2016. ICCD'16 paper selected as top ranked paper for publishing in IEEE TETC! June 2016. Being nominated for the Outstanding Graduate Student Award at UCSD! June-May 2016. Paper presentation GLSVLSI (Boston), DAC (Austin). May 2016. Defensed my proposal!! (PhD Candidate) March -May 2016. ISQED (Santa Clara), Research Expo'16 (La Jolla), ! January 2016. UCSD seminar class "From non-volatile memory to approximate computing" talk at [Slide]. June-November 2015. Paper presentation ICCAD[Slide] (Austin), Nanoarch (Boston), DAC (San Francisco)! May 2015. Research exam passed! March-April 2015. NVMW and Research Expo'15 presentation! |
Collaborators and Sponsors:
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