Hardware implementation of artificial neural networks (ANNs) using conventional binary arithmetic units requires large area and energy, due to the massive multiplication and addition operations in the inference process, limiting their use in edge computing and emerging Internet of Things (IoT) systems. Stochastic computing (SC), where the probability of 1s and 0s in a randomly generated bit-stream is used to represent a decimal number, has been devised as an alternative for compact and low-energy arithmetic hardware, due to its ability to implement basic arithmetic operations using far fewer logic gates than binary operations. To realize SC in hardware, however, tunable true random-number generators (TRNGs) are needed, which cannot be efficiently realized using existing complementary metal-oxide-semiconductor complementary metal-oxide-semiconductor (CMOS) technology. In this letter, we address this challenge by using magnetic tunnel junctions (MTJs) as TRNGs, the stochasticity of which can be tuned by an electric current via spin-transfer torque. We demonstrate the implementation of ANNs with SC units, using stochastic bit-streams experimentally generated by a series of 50 nm perpendicular MTJs. The numerical value (1 to 0 ratio) of the bit-streams is tuned by the current through the MTJs via spin-transfer torque with an ultralow current of <5 µA (= 0.25 MA⋅cm ^-2 ). The MTJ-based SC-ANN achieves 95% accuracy for handwritten digit recognition on the MNIST database. MRAM-based SC-ANNs provide a promising solution for ultra-low-power machine learning in edge, mobile, and IoT devices.

中文翻译：

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基于磁阻随机存取存储器的随机计算单元的人工神经网络实现

由于推理过程中的大量乘法和加法运算，使用常规二进制算术单元的人工神经网络（ANN）的硬件实现需要很大的面积和精力，从而限制了它们在边缘计算和新兴的物联网（IoT）系统中的使用。随机计算（SC）已被设计为紧凑型和低能耗算术硬件的替代方案，其中随机生成的比特流中1和0的概率代表十进制数，已被设计为一种替代方案算术运算使用的逻辑门比二进制运算少得多。但是，要在硬件中实现SC，就需要可调谐的真随机数生成器（TRNG），使用现有的互补金属氧化物半导体互补金属氧化物半导体（CMOS）技术无法有效实现这一目标。在这封信中，我们通过将磁性隧道结（MTJ）用作TRNG解决了这一挑战，其随机性可以通过自旋传递转矩通过电流来调节。我们使用由一系列50 nm垂直MTJ实验产生的随机比特流，演示了使用SC单元的ANN的实现。比特流的数值（1比0的比率）通过自耦传递扭矩通过MTJ的电流以<5 µA（= 0.25MA⋅cm）的超低电流进行调节 我们使用由一系列50 nm垂直MTJ实验产生的随机比特流，演示了使用SC单元的ANN的实现。比特流的数值（1比0的比率）通过自耦传递扭矩通过MTJ的电流以<5 µA（= 0.25MA⋅cm）的超低电流进行调节 我们使用由一系列50 nm垂直MTJ实验产生的随机比特流，演示了使用SC单元的ANN的实现。比特流的数值（1比0的比率）通过自耦传递扭矩通过MTJ的电流以<5 µA（= 0.25MA⋅cm）的超低电流进行调节 ^-2 ）。基于MTJ的SC-ANN在MNIST数据库上实现95％的手写数字识别精度。基于MRAM的SC-ANN为边缘，移动和IoT设备中的超低功耗机器学习提供了一个有前途的解决方案。