GS-IMTR is happy to announce the following presentation by Professor Masahiro Fujita of University of Tokyo.
The presentation will take place on 18 March, 14:00, in the Faculty Meeting room of the Computer Science department, Universitätsstr. 38, room 2.013 in the second floor.
Title: Use of two neural networks for more than two independent jobs and multi-node implementation of quantum circuit simulators
Abstract:
In this talk, we briefly discuss our two different works.
The first work explores and advances the potential of Implicit Neural Representation (INR) as a transformative approach to image compression. While previous research has employed INR to achieve compression by training small networks to reconstruct large images, this work proposes a novel advancement by representing multiple images with a single network. By modifying the loss function during training, the proposed approach allows a small number of sets of weights to represent a large number of images, even those significantly different from each other. Intensive experiments as well as analytical study of the convergence of this new training method are given to show the usefulness of the proposed approach.
The second work shows our decision diagram based quantum circuit simulator implementation on top of non-shared memory parallel (classical) computers. It achieves 26 times speed up over single node machines on the 38 qubits Shor’s algorithm for factorization, and can finish the simulation in 147 seconds. Also, it is shown that ring communication has a higher speed-up effect than broadcast communication even if the physical communication topology of the parallel computers is high-dimension mesh or torus, and the importance of selecting the appropriate automatic qubit swap insertion method is revealed which significantly speed up simulation.
Biography: Prof. Masahiro Fujita received the Ph.D. degree in information engineering from the University of Tokyo, Tokyo, Japan, in 1985. He joined Fujitsu, Tokyo, in 1985. From 1993 to 2000, he was assigned with Fujitsu Laboratories of America, Santa Clara, CA, USA, where he was the Director of the VLSI CAD Research Group. Since 2000, he has been a Professor with the VLSI Design and Education Center, University of Tokyo, until he retired in 2022, where he is currently a Researcher with the University of Tokyo. His research interests include hardware/software codesigns targeting embedded systems and formal analysis, verification, and synthesis of cyber–physical systems. Recently he is also working on hardware implementation of neural network-based information processing systems, such as learned image compression. Dr. Fujita has been a program and steering committee member in many prestigious international conferences and journals.