Abstract
Today, machine vision experiences large latency due to big data processing, which is a barrier to time-critical applications. To address this issue, in-sensor computing was presented in the past. Here, we present a scheme of computing in a magnetic tunneling junction (MTJ) sensor array for proof-of-principle. Using the MTJ sensor array, the functions of artificial neural network (ANN) classifiers and autoencoders were verified. The time for correct classification of one picture was less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9~\mu \text{s}$ </tex-math></inline-formula> . The power consumed in the sensor array can be decreased according to the square law without affecting the results. Our work shows universal circuits and algorithms to compute in resistance-style ANN image sensors with promising energy efficiency.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
