With the slowing down of Moore's law, augmenting complementary-metal-oxide
semiconductor (CMOS) transistors with emerging nanotechnologies (X) is becoming
increasingly important. In this paper, we demonstrate how stochastic magnetic
tunnel junction (sMTJ)-based probabilistic bits, or p-bits, can be combined
with versatile Field Programmable Gate Arrays (FPGA) to design an
energy-efficient, heterogeneous CMOS + X (X = sMTJ) prototype. Our
heterogeneous computer successfully performs probabilistic inference and
asynchronous Boltzmann learning despite device-to-device variations in sMTJs. A
comprehensive comparison using a CMOS predictive process design kit (PDK)
reveals that digital CMOS-based p-bits emulating high-quality randomness use
over 10,000 transistors with the energy per generated random number being
roughly two orders of magnitude greater than the sMTJ-based p-bits that
dissipate only 2 fJ. Scaled and integrated versions of our approach can
significantly advance probabilistic computing and its applications in various
domains, including probabilistic machine learning, optimization, and quantum
simulation.

本文提出了一种异构计算器，该计算器将基于随机磁隧道结的概率位（p-bits）与现有的 CMOS 技术相结合，恰当的设计提高了概率计算的能效，提供了比仿真高质量的随机比特。

CMOS + 随机纳米磁体：概率推理和学习的异质计算机

CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic  inference and learning

Extracting information from radiofrequency (RF) signals using artificial
neural networks at low energy cost is a critical need for a wide range of
applications. Here we show how to leverage the intrinsic dynamics of spintronic
nanodevices called magnetic tunnel junctions to process multiple analogue RF
inputs in parallel and perform synaptic operations. Furthermore, we achieve
classification of RF signals with experimental data from magnetic tunnel
junctions as neurons and synapses, with the same accuracy as an equivalent
software neural network. These results are a key step for embedded
radiofrequency artificial intelligence.

本文介绍了如何利用磁隧道结作为神经元和突触来处理多个模拟射频输入并执行突触操作，进而将射频信号分类，其精度与等效软件神经网络相同。这些结果是嵌入式射频人工智能的一个关键步骤。