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 interests include hardware and software co-design techniques for domain-specific silicon systems for emerging AI and data-intensive applications. He also bears a keen interest in agile chip development methodologies. 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. His research has been recognized through a NVIDIA Graduate PhD Fellowship, an IEEE SSCS Predoctoral Achievement Award, and distinguished paper awards at DAC and MICRO.

Academic Appointments

Contact

  • Academic
    ttambe@stanford.edu
    University - Faculty Department: Electrical Engineering Position: Assistant Professor

Additional Info

2024-25 Courses

Stanford Advisees

All Publications

  • 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

    View details for DOI 10.1145/3665314.3670813

    View details for Web of Science ID 001324640900056

  • 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

    View details for DOI 10.1109/JSSC.2022.3179303

    View details for Web of Science ID 000846283600001

  • 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

    View details for DOI 10.1109/DSN53405.2022.00031

    View details for Web of Science ID 000853348200018

  • 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

    View details for DOI 10.1145/3524059.3532359

    View details for Web of Science ID 001086201800001

  • 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

    View details for DOI 10.1145/3466752.3480095

    View details for Web of Science ID 001118047400060

  • 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

    View details for DOI 10.1145/3445814.3446746

    View details for Web of Science ID 000829871000047

  • 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., 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-+

    View details for DOI 10.1109/ISSCC42613.2021.9366062

    View details for Web of Science ID 000662193600058

  • 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

    View details for DOI 10.1109/dac18072.2020.9218516

    View details for Web of Science ID 000628528400028

  • 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

    View details for Web of Science ID 000621657500011

  • 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

    View details for DOI 10.1109/PACT.2019.00009

    View details for Web of Science ID 000550990200001

https://orcid.org/0000-0002-6411-9620