Angela Wang Bo

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Assistant Professor

Telephone: +65 6434 8202
Office #: 1.602-18
Pillar / Cluster: Information Systems Technology and Design
Research Areas:Artificial and Augmented Intelligence, Networked and Autonomous Systems


Dr. Wang Bo, Angela is an Assistant Professor at SUTD and a Principal Investigator at SUTD AI Centre since September 2020. Her research interests span various aspects of energy-efficient computing systems, architecture and circuit design, including on-device artificial intelligence, neuromorphic computing, biomedical wearables and ultra-low voltage memories. Particularly, her research work on biomedical wearables was featured by The Straits Times in 2020. She authored and co-authored many papers in prestigious journals and conferences, including JSSC, TCAS-I, TVLSI, MEJ, A-SSCC, MobiSys, DATE, etc. She has served as a reviewer for journals and conferences such as TCAS-I, TVLSI, TCAS-II, ICCAD and ISCAS. Since 2021, she has also served as an editorial board member of JCSC. She is a Track Session co-chair of EuCNC 2021. She was the recipient of IEEE Circuits & Systems Seoul Chapter Award in 2014.

Dr. Wang received her Ph.D. degree from Nanyang Technological University in 2015. Prior to joining SUTD, she was a research fellow, working with Prof. Peh Li-Shiuan (IEEE Fellow) at the National University of Singapore from 2016 to 2020. Before that, she was a staff engineer at MediaTek Singapore. She is an IEEE senior member.

About Neuromorphic Computing

Neuromorphic Computing deploys Spiking Neuron Network (SNN) models and learning rules to solve machine learning problems in a more energy-efficient way compared to conventional Artificial Neural Networks (ANN). SNN deploys all-or-nothing discrete spikes for layer-by-layer neuron communication. Similar to ANN, SNN is capable of inferencing a pre-defined problem with an analogy to biological neurons. A few ASICs, e.g. TrueNorth, Loihi, Zeroth from industry have emerged and been showcased their superiority in power and energy efficiency as next-generation AI hardware.

Our research group is working with Prof. Peh Li-Shiuan at NUS and other professors at SUTD to design and deliver (1) energy-efficient on-chip inference and training architectures and (2) ultra-low power edge AI systems, e.g. medical wearables by leveraging SNN. For any Ph.D. applicant with interest in such a research domain, please feel free to contact Prof. Wang via for more information. Familiarity with machine learning algorithms/frameworks, RTL design with Verilog/VHDL, and/or hardware-software co-design methodology will be a strong plus.

Research Collaborator


Media Coverage

“Track Your Health with Sensor Integrated into Smartwatch? No Sweat” by The Straits Times,

A low-power, highly responsive and reusable sweat pH monitor” by NUS News,                               

“How She Got There: Women in AI and Robotics” by SGInnovate,                                               


“Wearable Sweat Sensor”

  • International Patent Application PCT/SG2020/050689
  • Singapore Non-provisional Patent Application 11201911412T

Selected Publications

  • “REACT: A Heterogeneous Reconfigurable Neural Network Accelerator for Training and Inference on Edge Devices”, arXiv, 2021.
  • “Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip”, by B. Wang, J. Zhou, W. Wong and L. Peh. Design, Automation and Test in Europe Conference, Mar. 2020.
  • “HyCUBE: A 0.9V 26.4 MOPS/mW, 290 pJ/cycle, Power Efficient Accelerator for IoT Applications” by B. Wang, M. Karunaratne, A. Kulkarni, T. Mitra and L. Peh. IEEE Asian Solid-State Circuits Conference, Nov. 2019.
  • “pH Watch – Leveraging Pulse Oximeters in Existing Wearables for Reusable, Real-time Monitoring of pH in Sweat” by A. Balaji, C. Yuan, B. Wang, L. Peh and H. Shao. International Conference on Mobile Systems, Applications, and Services, Jun. 2019.
  • “Read bitline sensing and fast local write-back techniques in hierarchical bitline architecture for ultra-low voltage SRAMs” by B. Wang, Q. Li, and T. Kim. IEEE Transactions on Very Large Scale Integration Systems, vol 24, no. 6, 2016.
  • “Design of an ultra-low voltage 9T SRAM with equalized bitline leakage and CAM-assisted energy efficiency improvement” by B. Wang, T. Q. Nguyen, A. Do, J. Zhou. M. Je, and T. Kim. IEEE Transactions on Circuits and Systems-I, vol 62, no. 2, 2015.
  • “A 457-nW near-threshold cognitive multi-functional ECG processor CMOS for long-term cardiac monitoring” by X. Liu, J. Zhou, Y. Yang, B. Wang, J. Lan, C. Wang, J. Luo, W. L. Goh, T. Kim, and M. Je. IEEE Journal of Solid-State Circuits, vol 49, no. 11, 2014.
  • “0.2V 8T SRAM with Improved Bitline Sensing Using Column-based Data Randomization” by A. Do, Z. Lee, B. Wang, I. Chang, and T. Kim. IEEE Asian Solid-State Circuits Conference, Nov. 2014.
  • “A 0.18V charge-pumped DFF with 50.8% energy-delay reduction for near-/sub-threshold circuits” by B. Wang, J. Zhou, K. H. Chang, M. Je, and T. Kim. IEEE Asian Solid-State Circuits Conference, Nov. 2013.
  • “A 457-nW Cognitive Multi-Functional ECG Processor” by X. Liu, J. Zhou, Y. Yang, B. Wang, J. Lan, C. Wang, J. Luo, W. L. Goh, T. Kim, and M. Je. IEEE Asian Solid-State Circuits Conference, Nov. 2013.
  • “A 0.2V 16Kb 9T SRAM with bitline leakage equalization and CAM-assisted write performance boosting for improving energy efficiency” by B. Wang, T. Q. Nguyen, A. Do, J. Zhou. M. Je, and T. Kim. IEEE Asian Solid-State Circuits Conference, Nov. 2012.
  • “A 5.61 pJ, 16 kb 9T SRAM with Single-ended Equalized Bitlines and Fast Local Write-back for Cell Stability Improvement” by Q. Li, B. Wang, and T. Kim. IEEE European Solid-State Device Research Conference, Sep. 2012.

Research Interest

  • Algorithms, Architectures and Circuits of Neuromorphic Computing
  • Energy-Efficient AI Applications for Edge Computing Systems
  • AI Hardware Acceleration

Research Openings


2021 Summer

  • 01.117/99.502 Brain-Inspired Computing and Its Application

2021 Fall

  • 10.020 Data Driven World
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