Dr. Yanzhi Wang
Assistant Professor, 2015-present
329 Dana, 360 Huntington Avenue
Boston, MA 02115
Youtube channel and Bilibili channel:
Yanzhi Wang is currently an assistant professor in the Department of Electrical and Computer Engineering, and Khoury College of Computer Science (Affiliated) at Northeastern University. He has received his Ph.D. Degree in Computer Engineering from University of Southern California (USC) in 2014, under the supervision of Prof. Massoud Pedram. He received the Ming Hsieh Scholar Award (the highest honor in the EE Dept. of USC) for his Ph.D. study. He received his B.S. Degree in Electronic Engineering from Tsinghua University in 2009 with distinction from both the university and Beijing city.
Dr. Wang’s current research interests are the following. His group works on both algorithms and actual implementations (mobile and embeded systems, FPGAs, circuit tapeouts, GPUs, emerging devices, and UAVs).
- Real-time and energy-efficient deep learning and artificial intelligence systems
- Model compression of deep neural networks (DNNs)
- Neuromorphic computing and non-von Neumann computing paradigms
- Cyber-security in deep learning systems
For a brief list of technical achievements, his research (i) achieves and maintains the highest model compression rates on representative DNNs since 09/2018 (ECCV18, ASPLOS19, ICCV19, ISLPED19, ASP-DAC20, AAAI20-1, AAAI20-2, etc.), (ii) achieves, for the first time, real-time and fastest execution of representative large-scale DNNs on a mobile device (ASPLOS20, AAAI20, ICML19, IJCAI20, ECCV20, DAC20, CACM, etc.), (iii) achieves the highest performance/energy efficiency in DNN implementations on many platforms (FPGA19, ISLPED19 , AAAI19, HPCA19, ISSCC19, ASP-DAC20 , DATE20, AAAI20, PLDI20, ICS20, IJCAI20). It is worth mentioning that his work on AQFP superconducting based DNN inference acceleration, which is validated through cryogenic testing, has by far the highest energy efficiency among all hardware devices (ISCA19, ICCAD18).
His research works have been published broadly in top conference and journal venues, ranging from (i) EDA, solid-state circuit and system conferences such as DAC, ICCAD, DATE, ISLPED, FPGA, LCTES, ISSCC, etc., (ii) architecture and computer system conferences such as ASPLOS, ISCA, MICRO, HPCA, CCS, VLDB, PLDI, ICS, INFOCOM, ICDCS, etc., (iii) machine learning algorithm conferences such as AAAI, CVPR, ICML, ICCV, ICLR, IJCAI, ECCV, ACM MM, ICDM, etc., and (iv) IEEE and ACM transactions (including Communications of ACM) and Nature and Science series journals. He ranks No. 2 in CSRankings at Northeastern University in the past 10 years, and around No. 35 throughout the U.S. His research works have been cited for above 7,000 times according to Google Scholar with H-index 37. He has received four Best Paper Awards, has another ten Best Paper Nominations and four Popular Papers in IEEE TCAD. He received the U.S. Army Research Office Young Investigator Award. Besides, he has received Massachusetts Acorn Innovation Award, Google Equipment Research Award, MathWorks Faculty Award, MIT Tech Review TR35 China Finalist, Ming Hsieh Scholar Award, Young Student Support Award of DAC (for himself and six of his Ph.D. students), etc.
Yanzhi has delivered around 100 invited technical presentations on research of real-time and efficient deep learning systems. His research works have been broadly featured and cited in around 400 media, including Boston Globe, Communications of ACM, VentureBeat, The Register, Medium, The New Yorker, Wired, NEU News, Import AI, Italian National TV, Quartz, ODSC, MIT Tech Review, TechTalks, IBM Research Blog, ScienceDaily, AAAS, CNET, ZDNet, New Atlas, Tencent News, Sina News, to name a few.
The first Ph.D. student of Yanzhi, Dr. Caiwen Ding, has graduated in June 2019, and has become a tenure-track assistant professor in Dept. of CSE at University of Connecticut. The second Ph.D. student, Ning Liu, will start as a superstar employee at DiDi AI Research (DiDi Inc.). The third Ph.D. student, Ao Ren, will become a tenure-track assistant professor in Dept. of ECE at Clemson University. The fourth Ph.D. student, Ruizhe Cai, will join Facebook Infrastructure. The postdoc/visiting scholar, Chen Pan, will join Dept. of CSE at Texas A&M Corpus Christi, as tenure-track assistant professor.
Ph.D., Postdoc, and Visiting Scholar/Students Positions Available: Northeastern University has been rising thanks to the strong leadership and efforts from faculty members. The university is located in between the famous Museum of Fine Arts (MFA) and Boston Symphony and Berkelee College of Music, the Best Location at Boston! Please apply to NEU.
CoCoPIE (the Most Important Contribution):
Assuming hardware is the major constraint for enabling real mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning inference. Billions of dollars have been spent to fuel this intelligent hardware race. We challenge this assumption. By drawing on a recent real-time AI optimization framework CoCoPIE, it maintains that with effective compression-compiler co-design, it is possible to enable real-time artificial intelligence (AI) on mainstream end devices without special hardware.
The principle of compression-compilation co-design is to design the compression of Deep Learning Models and their compilation to executables in a hand-in-hand manner. This synergistic method can effectively optimize both the size and speed of Deep Learning models, and also can dramatically shorten the tuning time of the compression process, largely reducing the time to the market of AI products. CoCoPIE holds numerous records on mobile AI: the first time to support all kinds of DNNs including CNNs, RNNs, transformer and language models, etc.; the fastest DNN pruning and acceleration framework, up to 180X faster compared with current frameworks such as TensorFlow-Lite; a majority of representative DNNs and applications can be executed in real-time, for the first time, in off-the-shelf mobile devices; CoCoPIE framework on general-purpose mobile devices even outperforms a number of representative ASIC and FPGA solutions in terms of energy efficiency and/or performance.
Two Representative Contributions:
Yanzhi’s group has made the following two key contributions on DNN model compression and acceleration. The first is a systematic, unified DNN model compression framework (ECCV18, ASPLOS19, ICCV19, AAAI20-1, AAAI20-2, HPCA19, etc.) based on the powerful mathematical optimization tool ADMM (Alternating Direction Methods of Multipliers), which applies to non-structured and various types of structured weight pruning as well as weight quantization technique of DNNs. It achieves unprecedented model compression rates on representative DNNs, consistently outperforming competing methods. When weight pruning and quantization are combined, we achieve up to 6,645X weight storage reduction without accuracy loss, which is two orders of magnitude higher than prior methods. Our most recent results (on Arxiv) suggest that non-structured weight pruning is not desirable at any hardware platform.
Recently, the second major contribution has been made (ASPLOS20, AAAI20, ICML19, IJCAI20, ECCV20, DAC20, CACM, etc.) based on the ADMM solution framework. The compiler has been identified as the bridge between DNN algorithm-level compression and hardware-level acceleration, maintaining highest possible parallelism degree without accuracy compromise. Using mobile device (embedded CPU/GPU) as an example, we have developed a combination of pattern and connectivity pruning techniques, possessing both flexibility (and high accuracy) and regularity (and then hardware parallelism and acceleration). Accuracy and hardware performance are not a tradeoff anymore. Rather, it is possible for DNN model compression to be desirable at all of theory, algorithm, compiler, and hardware levels. For mobile devices, we achieve undoubtfully the fastest in DNN acceleration (e.g., 18.9ms inference time for VGG-16, 26ms for ResNet-50, and 5.4ms for MobileNet-V2 on a smartphone without accuracy loss), even outperforming prior work on FPGA and ASIC in many cases. All DNNs can be potentially be real-time in mobile devices through our algorithm-compiler-hardware co-design.
- 07/2020 [Paper] The CoCoPIE acceleration framework “CoCoPIE: Enabling Real-Time AI on Off-the-Shelf Mobile Devices via Compression-Compilation Co-Design” has been accepted by Communications of the ACM (CACM). Congrats!
- 07/2020 [Committee] Yanzhi will serve as committee member of ICLR 2021.
- 07/2020 [Talk] Yanzhi presents AQFP-based deep learning acceleration at IWLS 2020 (Youtube link).
- 07/2020 [Committee] Yanzhi will serve as committee member of WACV 2021.
- 07/2020 [Committee] Yanzhi will serve as committee member of MLCAD 2020.
- 07/2020 [Organizer] Yanzhi co-organizes the Road4NN Workshop colocated with DAC 2020.
- 07/2020 [Paper] Two collaborative papers accepted in ICCAD 2020, one on AQFP superconducting electronics physical design, and the other on neural network security. Another invited paper of ICCAD 2020 on medical applications of real-time deep learning acceleration.
- 07/2020 [Video] DAC presentation videos available online (Youtube link) (Bilibili link).
- 07/2020 [Student] Ph.D. student Ruizhe Cai has passed the dissertation defense and joins Facebook Inc.
- 07/2020 [Paper] The collaborative paper led by Kaidi on adversarial T-shirt has been accepted in ECCV as Spotlight paper. Congrats!
- 07/2020 [Paper] The automatic pattern generation and mobile DNN acceleration led by Xiaolong has been accepted in ECCV. Congrats!
- 06/2020 Yanzhi has received the U.S. Army Research Office Young Investigator Award.
- 06/2020 The CoCoPIE acceleration framework enables, for the first time, on-mobile real-time acceleration of 3D activity detection networks (e.g., C3D, R(2+1)D, S3D) using off-the-shelf mobile devices. We can achieve only 9ms per frame performance without accuracy loss, outperforming current frameworks by 30X speedup. Please see our demos.
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