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The field of nanomagnetism has recently attracted tremendous attention as it can potentially deliver low-power, high-speed and dense non-volatile memories. It is now possible to engineer the size, shape, spacing, orientation and composition of sub-100 nm magnetic structures. This has spurred the exploration of nanomagnets for unconventional computing paradigms. Here, we harness the energy-minimization nature of nanomagnetic systems to solve the quadratic optimization problems that arise in computer vision applications, which are computationally expensive. By exploiting the magnetization states of nanomagnetic disks as state representations of a vortex and single domain, we develop a magnetic Hamiltonian and implement it in a magnetic system that can identify the salient features of a given image with more than 85% true positive rate. These results show the potential of this alternative computing method to develop a magnetic coprocessor that might solve complex problems in fewer clock cycles than traditional processors.

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In this paper, we propose an improved quad Itoh-Tsujii algorithm to compute multiplicative inverse efficiently on Fieldprogrammable gate-arrays (FPGA) platforms for binary fields generated by irreducible trinomials. Efficiency is obtained by eliminating the precomputation steps required in conventional quad-ITA (QITA) scheme. Experimental results show that the proposed architecture improves the performance on FPGAs compared to existing techniques.

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