Bio


Thierry Tambe is an Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science, and the William George and Ida Mary Hoover Faculty Fellow at Stanford University. His research makes AI and emerging data-intensive applications run efficiently on domain-specific hardware via algorithm-to-silicon co-design. His work has been recognized through a Google ML and Systems Junior Faculty Award, a NVIDIA Graduate PhD Fellowship, an IEEE SSCS Predoctoral Achievement Award, and several distinguished paper awards. Previously, Thierry was a visiting research scientist at NVIDIA and an engineer at Intel. He received a B.S. and M.Eng. from Texas A&M University, and a PhD from Harvard University, all in Electrical Engineering.

Academic Appointments


Program Affiliations


  • Stanford SystemX Alliance

Professional Education


  • PhD, Harvard University, Electrical Engineering (2023)

Stanford Advisees


All Publications


  • EPOCHS-1: A 12 nm Highly Heterogeneous Open-Source SoC With Distributed Coin-Based Power Management and Integrated Hybrid Voltage Regulation IEEE JOURNAL OF SOLID-STATE CIRCUITS Zuckerman, J., Cochet, M., Cassel dos Santos, M., Jens Loscalzo, E., Swaminathan, K., Jia, T., Giri, D., Tambe, T., Jun Zhang, J., Buyuktosunoglu, A., Chiu, K., Di Guglielmo, G., Mantovani, P., Piccolboni, L., Tombesi, G., Trilla, D., Wellman, J., Yang, E., Amarnath, A., Jing, Y., Mishra, B., Park, J., Suresh, V., Zaliasl, S., Lekas, M., Cahill, S., Sadeghi, H., Meyer, J., Sturcken, N., Adve, S., Brooks, D., Wei, G., Shepard, K. L., Bose, P., Carloni, L. P. 2025
  • Towards Memory Specialization: A Case for Long-Term and Short-Term RAM Li, P., Abdurrahman, M., Cleaveland, R., Legtchenko, S., Levis, P., Stefanovici, I., Tambe, T., Tennenhouse, D., Trippel, C., Wong, H., ACM ASSOC COMPUTING MACHINERY. 2025: 27-36
  • Application-level Validation of Accelerator Designs Using a Formal Software/Hardware Interface ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS Huang, B., Lyubomirsky, S., Li, Y., He, M., Smith, G., Tambe, T., Gaonkar, A., Canumalla, V., Cheung, A., Wei, G., Gupta, A., Tatlock, Z., Malik, S. 2024; 29 (2)

    View details for DOI 10.1145/3639051

    View details for Web of Science ID 001193665600012

  • JointNF: Enhancing DNN Performance through Adaptive N:M Pruning across both Weight and Activation Zhang, S., Tambe, T., Wei, G., Brooks, D., ACM ASSOC COMPUTING MACHINERY. 2024
  • A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication and Fine-Grained Power Management IEEE International Solid-State Circuits Conference (ISSCC) Tambe, T., Zhang, J., Hooper, C., Jia, T., Whatmough, P., Zuckerman, J., Dos Santos, M., Loscalzo, E., Giri, D., Shepard, K., Carloni, L., Rush, A., Brooks, D., Wei, G. 2023
  • A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs IEEE JOURNAL OF SOLID-STATE CIRCUITS Tambe, T., Yang, E., Ko, G. G., Chai, Y., Hooper, C., Donato, M., Whatmough, P. N., Rush, A. M., Brooks, D., Wei, G. 2023; 58 (2): 569-581
  • GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators Mahmoud, A., Tambe, T., Aloui, T., Brooks, D., Wei, G., IEEE IEEE. 2022: 206-214
  • ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs Tan, C., Tambe, T., Zhang, J., Fang, B., Geng, T., Wei, G., Brooks, D., Tumeo, A., Gopalakrishnan, G., ACM ASSOC COMPUTING MACHINERY. 2022
  • EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference Tambe, T., Hooper, C., Pentecost, L., Jia, T., Yang, E., Donato, M., Sanh, V., Whatmough, P. N., Rush, A. M., Brooks, D., Wei, G., ACM ASSOC COMPUTING MACHINERY. 2021: 830-844
  • Robomorphic Computing: A Design Methodology for Domain-Specific Accelerators Parameterized by Robot Morphology Neuman, S. M., Plancher, B., Bourgeat, T., Tambe, T., Devadas, S., Reddi, V., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2021: 674-686
  • A 25mm<SUP>2</SUP> SoC for IoT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET Tambe, T., Yang, E., Ko, G. G., Chai, Y., Hooper, C., Donato, M., Whatmough, P. N., Rush, A. M., Brooks, D., Wei, G. edited by Fujino, L. C., Anderson, J. H., Belostotski, L., Dunwell, D., Gaudet, Gulak, Haslett, J. W., Halupka, D., Mirabbasi, S., Smith, K. C. IEEE. 2021: 158-+
  • Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference Tambe, T., Yang, E., Wan, Z., Deng, Y., Reddi, V., Rush, A., Brooks, D., Wei, G., IEEE IEEE. 2020
  • A 3mm<SUP>2</SUP> Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm Ko, G. G., Chai, Y., Donato, M., Whatmough, P. N., Tambe, T., Rutenbar, R. A., Brooks, D., Wei, G., IEEE IEEE. 2020
  • MASR: A Modular Accelerator for Sparse RNNs Gupta, U., Reagen, B., Pentecost, L., Donato, M., Tambe, T., Rush, A. M., Wei, G., Brooks, D., IEEE Comp Soc IEEE COMPUTER SOC. 2019: 1-14