PhD defense – Erwan Plouet

Erwan Plouet will defend his thesis entitled ‘Convolutional and dynamical spintronic neural networks’ on November 07th 2024 at 4pm in the TRT auditorium.

Abstract:

This thesis addresses the development of spintronic components for neuromorphic computing, a novel approach aimed at reducing the significant energy consumption of AI applications. The widespread adoption of AI, including very large scale langage models like ChatGPT, has led to increased energy demands, with data centers consuming about 1-2% of global power, and projected to double by 2030. Traditional hardware architectures, which separate memory and processing units, are not well-suited for AI tasks, as neural networks require frequent access to large in-memory parameters, resulting in excessive energy dissipation. Neuromorphic computing, inspired by the human brain, merges memory and processing capabilities in the same device, potentially reducing energy use. Spintronics, which manipulates electron spin rather than charge, offers components that can operate at lower power and provide efficient processing solutions.

The thesis is divided into two main parts. The first part focuses on the experimental implementation of a hybrid hardware-software convolutional neural network (CNN) using spintronic components. Spintronic synapses, which operate with radio frequency signals, enable frequency multiplexing to reduce the need for numerous physical connections in neural networks. This research work explores various designs of AMR spin diode-based synapses, each with different specificities, and demonstrates the integration of these synapses into a hardware CNN. A significant achievement was the implementation of a spintronic convolutional layer within a CNN that, when combined with a software fully-connected layer, successfully classified images from the FashionMNIST dataset with an accuracy of 88%, closely matching the performance of the pure software equivalent network. Key findings include the development and precise control of spintronic synapses, the fabrication of synaptic chains for weighted summation in neural networks, and the successful implementation of a hybrid CNN with experimental spintronic components on a complex task.

The second part of the thesis explores the use of spintronic nano oscillators (STNOs) for processing time-dependent signals through their transient dynamics. STNOs exhibit nonlinear behaviors that can be utilized for complex tasks like time series classification. A network of simulated STNOs was trained to discriminate between different types of time series, demonstrating superior performance compared to standard reservoir computing methods. We also proposed and evaluated a multilayer network architecture of STNOs for more complex tasks, such as classifying handwritten digits presented pixel-by-pixel. This architecture achieved an average accuracy of 89.83% similar to an equivalent standard continuous time recurrent neural network (CTRNN), indicating the potential of these networks to adapt to various dynamic tasks. Additionally, guidelines were established for matching device dynamics with input timescales, crucial for optimizing performance in networks of dynamic neurons. We demonstrated that multilayer networks of coupled STNOs can be effectively trained via backpropagation through time, highlighting the efficiency and scalability of spintronic neuromorphic computing.

This research demonstrated that spintronic networks can be used to implement specific architectures and solve complex tasks. This paves the way for the creation of compact, low-power spintronic neural networks that could be an alternative to AI hardware, offering a sustainable solution to the growing energy demands of AI technologies.