High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network
This work addresses the need for more efficient neuromorphic computing systems for AI applications, though it appears incremental as it builds on prior spintronic and memristor-based designs.
The paper tackles the problem of improving neuromorphic computing efficiency by introducing a fully spintronic Hopfield recurrent neural network, achieving enhanced speed and performance compared to existing neuromorphic architectures using emerging technologies.
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation. Recent research has demonstrated memristors and spintronic devices in various neural network designs boost efficiency and speed. This paper presents a biologically inspired fully spintronic neuron used in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the results are compared against those of current Hopfield neuromorphic architectures which use emerging technologies.