Yanzhi Wang

Assistant Professor,  Electrical and Computer Engineering

Contact

Office

  • 617.373.8805

Related Links

Research Focus

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

Education

  • PhD, University of Southern California, 2014

Honors & Awards

  • Army Research Office Young Investigator Award
  • Top Paper Award, IEEE Cloud Computing Conference (CLOUD)
  • System Design Contest Special Service Recognition Award at DAC
  • Massachusetts Acorn Innovation Award, Faculty Research Awards from Google, Mathworks, etc.
  • Best Paper Award, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
  • Best Paper Award, IEEE/ACM International Symposium on Low Power Electronic Design (ISLPED)

Research Overview

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

Selected Research Projects

  • CNS Core: Small: Collaborative: Content-based Viewport Prediction Framework for Live Virtual Reality Streaming
    • – Principal Investigator, National Science Foundation
  • PatDNN: Towards 100X Acceleration and Real-Time DNN Execution on Mobile Platforms
    • – Principal Investigator (YIP), Army Research Office
  • SPX: Collaborative Research: FASTLEAP: FPGA based Compact Deep Learning Platform
    • – Principal Investigator, National Science Foundation

Selected Publications

  • Niu, X. Ma, S. Lin, S. Wang, X. Qian, X. Lin, Y. Wang, B. Ren, PatDNN: Achieving Real-Time Dnn Execution on Mobile Devices with Pattern-Based Weight Pruning, in ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2020
  • Ma, F-M. Guo, W. Niu, X. Lin, J. Tang, K. Ma, B. Ren, Wang, PCONV: The Missing but Desirable Sparsity in Dnn Weight Pruning for Real-Time Execution on Mobile Device, AAAI Conference on Artificial Intelligence (AAAI), 2020
  • R. Cai, A. Ren, O. Chen, N. Liu, C. Ding, X. Qian, J. Han, W. Luo, N. Yoshikawa, Y. Wang, A Stochastic-Computing Based Deep Learning Framework Using Adiabatic Quantum-Flux-Parametron Superconducting Technology, in Proceedings of International Symposium on Computer Architecture (ISCA), 2019
Portrait of Wang

Faculty

Apr 15, 2021

Wang Receives IEEE TCSDM Early-Career Award

ECE Assistant Professor Yanzhi Wang received the IEEE TCSDM Early-Career Award for his “contribution to deep learning model compression and real-time, mobile deep learning AI acceleration for precise calibration.”

PhD

Mar 03, 2021

ECE PhD Student Wins 1st Place at CGO 2021

Computer Engineering PhD student Malith Jayaweera won First Place in the student research competition at the International Symposium on Code Generation and Optimization (CGO) 2021 for his research on “Data vs Instructions: Runtime Code Generation for Convolutions”.

Portrait of Wang

Faculty

Jan 08, 2021

Wang Receives Multiple Funding and Gift Awards from Industry

ECE Assistant Professor Yanzhi Wang received multiple funding and gift awards from industry, including DiDi USA, Kwai USA, Snap Inc., Perception Inc., and Tencent USA.

Portrait of Wang

Faculty

Jan 06, 2021

Wang organized the SpicyFL workshop at NeurIPS 2020

Yanzhi Wang, assistant professor at ECE, organized the SpicyFL Workshop on Scalability, Privacy, and Security in Federated Learning, at NeurIPS 2020. The workshop has 7 keynote talks, 1 panel, 10 paper presentations, and over 30 lightning talks.

Faculty

Sep 16, 2020

Using a Heterogeneous Multi-GPU Cluster to Support Exploration at Scale

ECE Professor David Kaeli and Assistant Professors Yanzhi Wang, Xue Lin, and Devesh Tiwari were awarded a $570K NSF grant for the “Acquisition of a Heterogeneous Multi-GPU Cluster to Support Exploration at Scale.”

Faculty

Aug 24, 2020

ECE Team won First Place in ISLPED 2020 Design Contest

ECE Assistant Professors Yanzhi Wang and Xue Lin and their graduate students were part of a collaborative Northeastern University and College of William and Mary team that won first place at the design contest at ISLPED 2020 for their demonstration titled, “CoCoPIE: A Framework of Compression-Compilation Co-design Towards Ultra-High Energy Efficiency and Real Time DNN Inference on Mobile Devices.”

Portrait of Wang

Faculty

Aug 24, 2020

Wang organized the ROAD4NN workshop at DAC 2020

Yanzhi Wang, assistant professor at ECE, organized the ROAD4NN workshop: International Workshop on Research Open Automatic Design for Neural Networks, at DAC 2020. Over 400 virtual attendees registered for the workshop.

prof next to network rack

Faculty

Jun 21, 2020

Yanzhi Wang Receives Army Young Investigator Award to Bring Deep Neural Network Machine Learning to Mobile Devices

ECE Assistant Professor Yanzhi Wang has been awarded a prestigious Young Investigator Program Award from the Army Research Office (ARO) on ultra-efficient, real-time DNN acceleration on mobile platforms. The ARO YIP is awarded to outstanding scientists beginning their independent careers to attract them to pursue fundamental research in areas relevant to the Army, to support their research in these areas, and to encourage their teaching and research careers.

Portrait of Wang

Faculty

Jun 15, 2020

Producing Ultra-High Performing and Energy-Efficient Electronics

ECE Assistant Professor Yanzhi Wang, in collaboration with Massoud Pedram from the University of Southern California, was awarded a $500K NSF grant for “Advanced Circuits, Architectures and Design Automation Technologies for Energy-efficient Single Flux Quantum Logic.”

Faculty

May 07, 2020

$1M DARPA Grant for Signal Processing in Neural Networks for Wireless IoT

ECE Associate Professor Kaushik Chowdhury, Assistant Professor Pau Closas, Professor Deniz Erdogmus, Professor Tommaso Melodia, and Assistant Professor Yanzhi Wang received $1M funding from DARPA for their project titled Signal Processing in Neural Networks (SPiNN) for Wireless IoT.

View All Related News