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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 undergraduate and 20 graduate students. I have published several papers at top-tier conferences and journals including computer architecture and electronic design/automation. I have received several awards during my study including the best Jacobs Graduate Student Council Award (2019), the Bernard and Sophia Gorden Engineering leadership award (2019), the best doctoral award for excellence in research (2018), and Jacobs school of engineering Powell Fellowship (2014-2017). |
Summary of Publications
The results of my PhD research have been published in top conferences and journals:
Selected Publications:
[ISCA'19] M. Imani, S. Gupta, Y. Kim, T. Rosing “FloatPIM: In-Memory Acceleration of Deep Neural Network Training with High Precision”, IEEE International Symposium on Computer Architecture (ISCA), 2019 (acceptance rate 17%) [PDF].
[CLOUD'19] M. Imani, Y. Kim, S. Riazi, J. Merssely, P. Liu, F. Koushanfar, T. Rosing “A Framework for Collaborative Learning in Secure High-Dimensional Space”, IEEE Cloud Computing (CLOUD), 2019 (acceptance rate 14.3%) [PDF].
[HPCA'17] M. Imani, A. Rahimi, D. Kong, T. Rosing, J. M. Rabaey “Exploring Hyperdimensional Associative Memory”, IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2017 [PDF].
[DATE'16] M. Imani, A. Rahimi, T. Rosing, "Resistive Configurable Associative Memory for Approximate Computing" IEEE/ACM Design Automation and Test in Europe Conference (DATE), 2016 [PDF].
- Computer Architecture (ISCA, HPCA, IEEE TC),
- Electronic Design (DAC, DATE, ISLPED, FPGA, FCCM),
- Computer Aided Design (ICCAD, TCAD)
Selected Publications:
[ISCA'19] M. Imani, S. Gupta, Y. Kim, T. Rosing “FloatPIM: In-Memory Acceleration of Deep Neural Network Training with High Precision”, IEEE International Symposium on Computer Architecture (ISCA), 2019 (acceptance rate 17%) [PDF].
[CLOUD'19] M. Imani, Y. Kim, S. Riazi, J. Merssely, P. Liu, F. Koushanfar, T. Rosing “A Framework for Collaborative Learning in Secure High-Dimensional Space”, IEEE Cloud Computing (CLOUD), 2019 (acceptance rate 14.3%) [PDF].
[HPCA'17] M. Imani, A. Rahimi, D. Kong, T. Rosing, J. M. Rabaey “Exploring Hyperdimensional Associative Memory”, IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2017 [PDF].
[DATE'16] M. Imani, A. Rahimi, T. Rosing, "Resistive Configurable Associative Memory for Approximate Computing" IEEE/ACM Design Automation and Test in Europe Conference (DATE), 2016 [PDF].
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.
Processing In-Memory:
Many machine learning and big data processing applications require to process a large amount of data. 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 that dominate the total computation cost. We design novel architectures which enables Processing In-Memory (PIM), where data can be processed locally in memory without using any processing cores. In contrast to prior method that enable dot product operation in analog domain, we design the first digital-based PIM architecture compatible with digital data and the recently released memory by Intel (3D Xpoint),
Many machine learning and big data processing applications require to process a large amount of data. 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 that dominate the total computation cost. We design novel architectures which enables Processing In-Memory (PIM), where data can be processed locally in memory without using any processing cores. In contrast to prior method that enable dot product operation in analog domain, we design the first digital-based PIM architecture compatible with digital data and the recently released memory by Intel (3D Xpoint),
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 more than 20 undergrad students and 20 graduate students, including 8 PhD students. I was honored to mentor about 14 female students and have my small contribution to increase diversity in engineering and computing fields. I have published more than 27 papers in the top venue conferences/journals with undergraduates where more than 18 are with minorities. I have also volunteered to participated in ERSP (2018, 2019) and ENLACE (2017, 2018) programs which aim to support undergraduate and the research of Latino students at UC San Diego. More information about my Mentorship and contribution to Diversity.
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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 ($40M 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) NSF REU: Research Experiences for Undergraduates based on Grant 1527034 |
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What's New?
New! May. 2019. Two accepted papers in ISLPED'19! New! April. 2019. Bernard and Sophia Gordon Engineering Leadership award, the highest leadership award in the entire engineering departments at UC San Diego! Mar. 2019. Accepted papers in ISCA'19 (acceptance rate 17%)! Feb. 2019. Accepted papers in FCCM'19 and BHI'19 conferences! Feb. 2019. Outstanding Graduate Student Award in the entire engineering departments at UC San Diego! Recognized as exceptional graduate students that have made significant contributions to the school of the engineering! Feb. 2019. Four papers accepted in DAC'19! Jan. 2019. Accepted paper in CLOUD (acceptance rate 14.3%)! Dec. 2018. Accepted papers in IEEE TC, ACM JETC, and IEEE D&T journals!! Dec. 2018. Accepted for DATE 2019 PhD Forum! 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. 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 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 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! 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! May 2016. Defensed my proposal!! (PhD Candidate) May 2015. Research exam passed! |
Collaborators and Sponsors:
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