Amir Yazdanbakhsh
I joined Google Research as a Research Scientist in 2019, following a one year AI residency. I am the co-founder and co-lead of the Machine Learning for Computer Architecture team. We leverage the recent machine learning methods and advancements to innovate and design better hardware accelerators. The work of our team has been covered by media outlets including ZDNet and InfoQ. I am also interested in designing large-scale distributed systems for training machine learning applications. To that end, I led the development of a massively large-scale distributed reinforcement learning system that scales to TPU Pod and efficiently manages thousands of actors to solve complex, real-world tasks. As a case study, our team demonstrates how using this highly scalable system enables reinforcement learning to accomplish chip placement in ~an hour instead of days or weeks by human effort. I received my Ph.D. degree in computer science from the Georgia Institute of Technology. My Ph.D. work has been recognized by various awards, including Microsoft PhD Fellowship and Qualcomm Innovation Fellowship.
Authored Publications
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ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
Haoran You
Yipin Guo
Yichao Fu
Wei Zhou
Huihong Shi
Souvikk Kundu
Yingyan Lin
38th Annual Conference on Neural Information Processing Systems (NeurIPS) (2024)
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Mangpo Phothilimthana
Thirimadura C. Yasendra Mendis
2022 IEEE International Symposium on Workload Characterization (2022) (to appear)
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
Sheng-Chun Kao
Shivani Agrawal
Suvinay Subramanian
Tushar Krishna
(2022) (to appear)
Data-Driven Offline Optimization for Architecting Hardware Accelerators
Aviral Kumar
Sergey Levine
International Conference on Learning Representations 2022 (to appear)
ReLeQ: A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks
Ahmed Taha Elthakeb
Prannoy Pilligundla
Fatemeh Mireshghallah
Hadi Esmaeilzadeh
IEEE Micro (2020)
Efficient Imitation Learning with Local Trajectory Optimization
Jialin Song
Navdeep Jaitly
Azalia Mirhoseini
ICML 2020 Workshop on Inductive Biases, Invariances and Generalization in RL (2020)
Apollo: Transferable Architecture Exploration
Albin Jones
Ravi Narayanaswami
Sat Chatterjee
ML for Systems Workshop at NeurIPS 2020
