This paper introduces an analog-to-stochastic converter using a magnetic tunnel junction (MTJ) device for vision chips based on stochastic computation. Stochastic computation has been recently exploited for area-efficient hardware implementation, such as low-density parity-check (LDPC) decoders and image processors. However, power-and-area hungry two-step (analog-to-digital and digital-to-stochastic) converters are required for the analog to stochastic signal conversion. To realize a one-step conversion, an MTJ device is used as it inherently exhibits a probabilistic switching behavior between two resistance states. Exploiting the device-based probabilistic behavior, analog signals can be directly and area-efficiently converted to stochastic signals to mitigate the signal-conversion overhead. The analog-to-stochastic signal conversion is theoretically described and the conversion characteristic is evaluated using device and circuit parameters. In addition, the resistance variability of the MTJ device is considered in order to compensate the variability effect on the signal conversion. Based on the theoretical analysis, the analog-to-stochastic converter is designed in 90nm CMOS and 100nm MTJ technologies and is verified using a SPICE simulator (NS-SPICE) that handles both transistors and MTJ devices.

翻译：本文介绍了一种利用磁隧道结（MTJ）器件、面向基于随机计算的视觉芯片的模数随机转换器。随机计算最近已被用于实现面积高效的硬件，例如低密度奇偶校验（LDPC）解码器和图像处理器。然而，模拟信号到随机信号的转换需要功耗和面积开销较大的两步（模数转换和数随机转换）转换器。为实现一步转换，本文采用了MTJ器件，因为它固有地表现出两种电阻状态之间的概率性切换行为。利用这种基于器件的概率行为，模拟信号可以直接且面积高效地转换为随机信号，从而降低信号转换开销。本文从理论上描述了模数随机信号转换过程，并利用器件和电路参数评估了转换特性。此外，还考虑了MTJ器件的电阻可变性，以补偿其对信号转换的影响。基于理论分析，该模数随机转换器采用90纳米CMOS和100纳米MTJ技术设计，并使用可同时处理晶体管和MTJ器件的SPICE仿真器（NS-SPICE）进行了验证。