Bi-layered zinc oxide (ZnO) and bismuth substituted yttrium iron garnet (Bi:YIG) was fabricated and magneto-optically investigated. Enhancement of Faraday rotation and magnetic circular dichroism (MCD) was observed. The wavelength of MCD enhancement was in good agreement with exciton wavelength of ZnO. This enhancement was only observed in the bi-layer, and implies that the exciton generated in ZnO interacted with Bi:YIG. Because the exciton wavelength of ZnO can be controlled by electro-optic effect, this result has the potential for realizing voltage control of magneto-optic effect.
A binarized neural network (BNN) accelerator based on a processing/computing-in-memory architecture using ultralow-voltage retention SRAM (ULVR-SRAM) is proposed for the energy minimum-point (EMP) operation. The BNN accelerator (BNA) macro is designed to perform stable inference operations at EMP and substantive power-gating using ULVR at an ultralow voltage (< EMP), which can be applied to fullyconnected layers with arbitrary shapes and sizes. The EMP operation of the BNA macro, which is enabled by applying the ULVR-SRAM to the macro, can dramatically improve the energy efficiency (TOPS/W) and significantly enlarge the number of parallelized multiply-accumulate (MAC) operations. In addition, the ULVR mode of the BNA macro, which also benefits from the usage of ULVR-SRAM, is effective at reducing the standby power. The proposed BNA accelerator macro can show a high energy efficiency of 65 TOPS/W for fully connected layers. This BNA macro concept using the ULVR-SRAM can be expanded to convolution layers, where the EMP operation is also expected to enhance the energy efficiency of convolution layers.
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