Spin-Torque-Nano-Oscillator based neuromorphic computing assisted by laser

Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of second. The most promising solution, which has been recently researched widely, is brain-inspired computers, so-called neuromorphic computing systems (NCS). The NCS overcomes the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain. Recently, spintronic-based NCSs have shown the potential of implementation of low-power highdensity NCSs, where neurons are implemented using magnetic tunnel junctions (MTJs) or spin torque nano-oscillators (STNOs) and memristors are used to mimic synaptic functionality. Although using STNOs as neuron requires lower energy in comparison to the MTJs, still there is a huge gap between the power consumption of spintronic-based NCSs and the brain due to high bias current needed for starting the oscillation with a detectable output power. In this manuscript, we propose a spintronic-based NCS (196 × 10) proof-of-concept where the power consumption of the NCS is reduced by assisting the STNO oscillation through a microwatt nanosecond laser pulse. The experimental results show the power consumption of the STNOs in the designed NCS is reduced by 55.3% by heating up the STNOs to 100 • C. Moreover, the average power consumption of spintronic layer (STNOs and memristor array) is decreased by 54.9% at 100 • C compared with room temperature. The total power consumption of the proposed laser assisted STNO-based NCS (LAO-NCS) at 100 • C is improved by 40% in comparison to a typical STNO-based NCS at room temperature. Finally, the energy consumption of the LAO-NCA at 100 • C is expected to reduce by 86% compared with a typical STNO-based NCS at the room temperature.

show abstract

“…This manuscript is an extension of a previously published conference manuscript (Farkhani et al, 2019a).…”

Section: Data Availability Statementmentioning

confidence: 92%

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

Joksas

AlMutairi

Lee

et al. 2022

Advanced Intelligent Systems

View full text Add to dashboard Cite

In a data-driven economy, virtually all industries benefit from advances in information technology-powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if the discrepancy between the current computing power demands and what the existing technologies can offer is not addressed. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal-oxide-semiconductor (CMOS) technologies, such as transistors. Herein, three approaches that will likely play an essential role in future computing systems are discussed: memristive electronics, spintronics, and electronics based on 2D materials. The authors present how these technologies may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.

show abstract

Spin-Torque-Nano-Oscillator based neuromorphic computing assisted by laser

Cited by 2 publications

References 31 publications

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

LAO-NCS: Laser Assisted Spin Torque Nano Oscillator-Based Neuromorphic Computing System

Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

Contact Info

Product

Resources

About