| 研究実績の概要 |
So far, as planned, I developed an artificial neuron using ferroelectric tunnel junctions and a set of tools for studying its behaviour in recurrent neural networks (RNN). These neurons are based on a physical mechanism radically different than conventional electronics: resistive switching by controlling the polarization reversal in the ferroelectric barrier. It makes them much smaller and energy efficient to interact with human-timescale signals.
For developing the neurons, I designed and build ad-hoc electric instrumentation required to characterize the devices with neuromorphic signals. Then, I developed a concentrated parameters model, and finally, the whole circuit. They integrate, which is a crucial function in neurons, by gradually switching the ferroelectric barrier.
I also developed a (simulated) RNN, based on a pool of 1000 neurons with sparse connectivity. Before training, the system behaves chaotic. I then implemented a training algorithm that allows it to reproduce arbitrary sequences. For the training, the synaptic connections to a selected output neuron are tune by supervised learning. I assembled a computer system for doing simulations requiring both intense GPU and CPU computations, which was necessary for training.
In the second part of the project, based on these neurons, I will study the stability of RNN’s, develop architectures to reduce energy requirements, and develop instrumentation to implement and characterize neuromorphic systems.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
I proposed 3 types of research, namely:
1) Fabrication, modelling and characterization of ferroelectric tunnel junctions (FTJ).
2) Design of a neuron using FTJ.
3) Design an RNN using the FTJ-based neurons, including software for training and inference.
I completed 100% of these 3 researchers.
|
