Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.
Temperature sensors are becoming an increasingly important component in System-on-Chip (SoC) designs with increasing transistor scaling, power density and associated heating effects. This work explores a compact nanoelectronic temperature sensor based on a Magnetic Tunnel Junction (MTJ) structure. The MTJ switches probabilistically depending on the operating temperature in the presence of thermal noise. Performance evaluation of the proposed MTJ temperature sensor, based on experimentally measured device parameters, reveals that the sensor is able to achieve a conversion rate of 2.5K samples/s with energy consumption of 8.8 nJ per conversion (1–2 orders of magnitude lower than state-of-the-art CMOS sensors) for a linear sensing regime of 200–400 K.
In this article, we present a comprehensive study of four frequency locking mechanisms in Spin Torque Nano Oscillators (STNOs) and explore their suitability for a class of specialized computing applications. We implemented a physical STNO model based on Landau-Lifshitz-Gilbert-Slonczewski equation and benchmarked the model to experimental data. Based on our simulations, we provide an in-depth analysis of how the “self-organizing” ability of coupled STNO array can be effectively used for computations that are unsuitable or inefficient in the von-Neumann computing domain. As a case study, we demonstrate the computing ability of coupled STNOs with two applications: edge detection of an image and associative computing for image recognition. We provide an analysis of the scaling trends of STNOs and the effectiveness of different frequency locking mechanisms with scaling in the presence of thermal noise. We also provide an in-depth analysis of the effect of variations on the four locking mechanisms to find the most robust one in the presence of variations.
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