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You can find my CV here.
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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 am an author of more than 85 publications at top tier conferences and journals. My contributions resulted in over $40M grants funded from multiple governmental agencies (four NSF grants, three SRC grants) and several companies including IBM, Intel, and Micron. I have received the most prestigious awards from the UCSD school of engineering including the Gordon Engineering Leadership Award (2019) and the Outstanding Graduate Research Award (2019), as well as the Best Graduate Student Award from the Computer Science department (2018). I also nominations for several best paper awards including DAC 2019, ISQED 2018, and ICCD 2016 conferences. I will be in the academic job market this year. |
Summary of Publications
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The results of my PhD research have been published in top conferences and journals:
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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 [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 [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].
[ISLPED'16] M. Imani, Y. Kim, A. Rahimi, T. Rosing, "ACAM: Approximate Computing Based on Adaptive Associative Memory with Online Learning" IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2016 [PDF].
[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 [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 [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].
[ISLPED'16] M. Imani, Y. Kim, A. Rahimi, T. Rosing, "ACAM: Approximate Computing Based on Adaptive Associative Memory with Online Learning" IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2016 [PDF].
Research Projects
Brain-Inspired Hyperdimensional Computing:
To achieve real-time performance with high energy-efficiency, we rethink not only how we accelerate machine learning algorithms in hardware, but also we redesign the algorithms themselves using strategies that more closely model the ultimate efficient learning machine: the human brain. My Ph.D. research developed brain-inspired HyperDimensional (HD) computing as an interdisciplinary research area emerged from theoretical neuroscience. HD computing is motivated by the understanding that the human brain operates on high-dimensional representations of data originated from the large size of brain circuits. It thereby models the human memory using points of a high-dimensional space. It mimics several important functionalities of the human memory model with vector operations which are computationally tractable, but mathematically rigorous in describing human cognition.
To achieve real-time performance with high energy-efficiency, we rethink not only how we accelerate machine learning algorithms in hardware, but also we redesign the algorithms themselves using strategies that more closely model the ultimate efficient learning machine: the human brain. My Ph.D. research developed brain-inspired HyperDimensional (HD) computing as an interdisciplinary research area emerged from theoretical neuroscience. HD computing is motivated by the understanding that the human brain operates on high-dimensional representations of data originated from the large size of brain circuits. It thereby models the human memory using points of a high-dimensional space. It mimics several important functionalities of the human memory model with vector operations which are computationally tractable, but mathematically rigorous in describing human cognition.
Big Data Processing Acceleration:
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 PIM functionality in analog domain, we design the first digital-based PIM architecture that (i) works on digital data; thus, it eliminates ADC/DAC blocks that dominate the area. (ii) it addresses internal data movement issue by enabling in-place computation where the big data is stored, (iii) it natively supports floating-point precision that is essential for many scientific applications, (iv) it is compatible with any bipolar memory technology, including 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 PIM functionality in analog domain, we design the first digital-based PIM architecture that (i) works on digital data; thus, it eliminates ADC/DAC blocks that dominate the area. (ii) it addresses internal data movement issue by enabling in-place computation where the big data is stored, (iii) it natively supports floating-point precision that is essential for many scientific applications, (iv) it is compatible with any bipolar memory technology, including Intel 3D XPoint.
Approximate Computing:
I proposed a novel architecture with software and hardware support for approximate computing. Hardware components are enhanced with the ability to dynamically adapt approximation at a quantifiable and controllable cost in terms of accuracy. Software services complement hardware to ensure the 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.
I proposed a novel architecture with software and hardware support for approximate computing. Hardware components are enhanced with the ability to dynamically adapt approximation at a quantifiable and controllable cost in terms of accuracy. Software services complement hardware to ensure the 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) SRC Grant: Automated Task Scheduling for Programmable Processing-in-Memory Systems ($100,000 Grants) |
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NSF #1911095: FET: Small: Brain-Inspired Hyperdimensional Computing for IoT Applications (Awarded Amount: $500,000)
NSF #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 CC #1826967: NPEO: Toward the National Research Platform (Awarded Amount: $2,516,000) NSF REU: Research Experiences for Undergraduates based on Grant 1527034 |
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What's New?
New! July. 2019. Session Chair in ISLPED'19! New! June. 2019. Two accepted papers in ICCAD'19! New! June. 2019. Best Poster Award, DAC PhD Forum, 2019! New! June. 2019. Best paper candidate at DAC 2019! New! May. 2019. Honored to receive Goren Leadership award (Video, photos)! New! May. 2019. the Bernard and Sophia Gordon Engineering Leadership award, the most prestigious leadership award in the school of engineering at UC San Diego (check here)! New! May. 2019. Two accepted papers in ISLPED'19! 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 school of 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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