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Dr. Yanzhi Wang

Assistant Professor, 2015-present

Department of Electrical & Computer Engineering, College of Engineering,

Khoury College of Computer Science (Affiliated),

Northeastern University

B.S. (Tsinghua), Ph.D. (University of Southern California)

329 Dana, 360 Huntington Avenue
Boston, MA 02115
Phone: 617.373.8805
Email: yanz.wang@northeastern.edu

Youtube channel and Bilibili channel:


About:

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, PACT20). 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, PACT, 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 JSSC) 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 7,900 times according to Google Scholar with H-index 40. 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, his group 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), DAC Service Award, etc. His group and students have received first place in ISLPED Design Contest twice (2012, 2020), and awards in multiple other contests such as Low Power Computer Vision Challenge 2019 and NeurIPS MicroNet Challenge 2019.

Yanzhi has delivered over 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 450 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.

More Info about CoCoPIE: Official webpage https://www.cocopie.ai/; CoCoPIE Youtube Channel here and Bilibili Channel here; CoCoPIE description paper and demonstration paper.


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.


Recent News:  

  • 11/2020 [Paper] Tianyun’s paper “StructADMM: Achieving Ultra-High Efficiency in Structured Pruning for DNNs” has been accepted by IEEE TNNLS (Impact Factor 12.18).
  • 11/2020 [Media] Our research on mobile deep learning acceleration has been featured in the SMART Center interview on MRS TV at the 2020 MRS Virtual Spring/Fall Meeting & Exhibit (Link).
  • 11/2020 [Paper] One invited paper on Machine Learning for Hardware System Control in Proc. of IEEE.
  • 11/2020 [Paper] Our paper accepted in NeurIPS 2020 workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL).
  • 11/2020 [Paper] Two research papers, one on AQFP superconducting circuits placement and routing, and another on DNN compression for memristor crossbar acceleration, accepted in Proc. of DATE 2021.
  • 11/2020 [Grant] Yanzhi’s group has received a research gift grant from Kwai Inc. U.S. Thanks Kwai!
  • 11/2020 [Grant] Yanzhi’s group has received a research grant from DiDi U.S. Thanks DiDi!
  • 11/2020 [Paper] Our collaborative paper accepted in ISSCC 2021 Student Research Symposium.
  • 11/2020 [Talk] Yanzhi remotely presented compression-compilation co-design for real-time DNN acceleration at HALO workshop with ICCAD, 2020.
  • 10/2020 [Paper] Our research paper “Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework” accepted in Proc. of HPCA 2021.
  • 10/2020 [Paper] One demonstration paper accepted in AAAI 2021.
  • 10/2020 [Paper] Our paper accepted in NeurIPS 2020 workshop on autonomous driving.
  • 10/2020 [Committee] Yanzhi will serve as committee member of DAC 2021.
  • 10/2020 [Paper] Collaborative paper “An Actor-Critic-based Transfer Learning Framework for Experience-driven Networking” accepted in IEEE/ACM Trans. on Networking (TON) (Impact Factor 5.1).
  • 10/2020 [Paper] Collaborative paper “ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning” accepted in IEEE Trans. on Mobile Computing (TMC).
  • 10/2020 [Media] Our CoCoPIE for real-time BERT acceleration on mobile devices is reported in Medium (link), also in Mc.ai (link)
  • 10/2020 [Talk] Yanzhi remotely presented compression-compilation co-design at ByteDance.
  • 10/2020 [Committee] Yanzhi will serve as committee member of CVPR 2021.
  • 09/2020 [Paper] Collaborative paper on block-circulant matrix-based DNN acceleration chip accepted in JSSC (Journal of Solid-State Circuits).
  • 09/2020 [Paper] Collaborative paper on SRAM-based process-in-memory for DNN acceleration accepted in JSSC (Journal of Solid-State Circuits).
  • 09/2020 [Paper] Kaidi’s paper accepted in NeurIPS 2020. Congrats to Kaidi!
  • 09/2020 [Media] Our CoCoPIE for YoLoBile: real-time YoLo-v4 acceleration on mobile devices is reported in CVer (link), also cited in Tencent News.
  • 09/2020 [Media] Our CoCoPIE for real-time BERT acceleration on mobile devices is reported in CSDN (link), also cited in Tencent News.
  • 08/2020 [Award] Our CoCoPIE mobile acceleration framework received first place in ISLPED Design Contest 2020 (Youtube Link) (BiliBili Link).
  • More news

Research Sponsors: