The 3rd D2T Special Seminar 2022 ONLINE
2022.11.11 Fri. 3:30pm- JST

Zoom meeting/Hybrid(学内限定)
Language: English


D2T seminar in November hosts two distinguished researchers from US.

The first talk discusses advanced logic circuit synthesis techniques by which logic circuit operations can become faster by 20% or more. The idea is to unify logic optimization, transformation into cells in libraries, and sequential circuit optimization, and recently implemented new tools significantly improve the performance of the synthesized circuits.

The second talk discusses one of the most important issues in deep learning: why learning results by deep learning have generalization not just memorizing each training pattern. That is, it reasonably works well as well for new inputs which are never seen in the training phase. This is the key feature of deep learning, and the talk gives a way to explain it and proposes future directions.

Free registration:
Register here

After registrating, you will recieve a confirmation e-mail containing information about joining the seminar.
We use the Zoom meeting system. If you don't reach the mail after the registration, please contact D2T research department.

Chairperson: Masahiro Fujita and Akio Higo, (d.lab the Univ. of Tokyo)
Integrating Logic Synthesis, Technology Mapping, and Retiming

Dr. Alan Mishchenko

This talk presents a method that combines logic synthesis, technology mapping, and retiming into a single integrated flow. The proposed integrated method is applicable to both standard cell and FPGA designs. An efficient implementation uses sequential And- Inverter Graphs (AIGs). Experiments on a variety of industrial circuits from the IWLS 2005 benchmarks show an average reduction of the clock period of 25%, compared to the traditional mapping without retiming, and by 20%, compared to traditional mapping followed by retiming applied as a post-processing step.

Speaker Bio:
Alan graduated with M.S. from Moscow Institute of Physics and Technology (Moscow, Russia) in 1993 and received his Ph.D. from Glushkov Institute of Cybernetics (Kiev, Ukraine) in 1997. In 2002, Alan joined the EECS Department at University of California, Berkeley, where he is currently a full researcher. His research is in computationally efficient logic synthesis, formal verification, and machine learning.

On the Generalization Mystery in Deep Learning

Dr. Satrajit Chatterjee

The generalization mystery in deep learning is the following: Why do over-parameterized neural networks trained with gradient descent (GD) generalize well on real datasets even though they are capable of fitting random datasets of comparable size? Furthermore, from among all solutions that fit the training data, how does GD find one that generalizes well (when such a well-generalizing solution exists)? We argue that the answer to both questions lies in the interaction of the gradients of different examples during training. Intuitively, if the per-example gradients are well-aligned, that is, if they are coherent, then one may expect GD to be (algorithmically) stable, and hence generalize well. We formalize this argument with an easy to compute and interpretable metric for coherence, and show that the metric takes on very different values on real and random datasets for several common vision networks. The theory also explains a number of other phenomena in deep learning, such as why some examples are reliably learned earlier than others, why early stopping works, and why it is possible to learn from noisy labels. Moreover, since the theory provides a causal explanation of how GD finds a well-generalizing solution when one exists, it motivates a class of simple modifications to GD that attenuate memorization and improve generalization. Generalization in deep learning is an extremely broad phenomenon, and therefore, it requires an equally general explanation. We conclude with a survey of alternative lines of attack on this problem, and argue that the proposed approach is the most viable one on this basis.


Speaker Bio
Sat is an Engineering Leader and Machine Learning Researcher who was at Google AI till recently. His current research focuses on fundamental questions in deep learning (such as understanding why neural networks generalize at all) as well as various applications of ML (ranging from hardware design and verification to quantitative finance). Before Google, he was a Senior Vice President at Two Sigma, a leading quantitative investment manager, where he founded one of the first successful deep learning-based alpha research groups on Wall Street and led a team that built one of the earliest end-to-end FPGA-based trading systems for general purpose ultra-low latency trading. Prior to that, he was a Research Scientist at Intel where he worked on microarchitectural performance analysis and formal verification for on-chip networks. He did his undergraduate studies at IIT Bombay, has a PhD in Computer Science from UC Berkeley, and has published in the top machine learning, design automation, and formal verification conferences.

Past D2T Special Seminar ONLINE

東京大学 大学院工学系研究科 附属システムデザイン研究センター アドバンテストD2T寄附講座
東京大学 大学院工学系研究科 附属システムデザイン研究センター 基盤デバイス研究部門
東京大学 大学院工学系研究科 附属システムデザイン研究センター アドバンテストD2T寄附講座
東京大学 大学院工学系研究科 附属システムデザイン研究センター 基盤デバイス研究部門
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