Probabilistic Neural Computing with Stochastic Devices
Probabilistic Neural Computing with Stochastic Devices
复制标题
使用随机设备的概率神经计算
DOI：
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
J. Aimone
中科院分区：
文献类型：
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作者：
Shashank Misra;Leslie C. Bland;S. Cardwell;J. Incorvia;Conrad D. James;Andrew D. Kent;Catherine D. Schuman;J. D. Smith;J. Aimone
大脑有效地证明了计算体系结构开发的有力灵感，其中处理与内存紧密整合，通信是事件驱动的，并且可以按大规模进行模拟计算。科学计算和人工智能应用的速度。将神经形态计算的范围扩展到迄今为止的概率应用，许多努力主要集中在一个微电子堆栈的一个规模上，例如在确定性硬件上实施概率算法通过最终有问题的架构杠杆作用。连接和隧道二极管可以在随机状态下进行操作，并将其纳入可扩展的神经形态架构中，这些结构可以影响许多概率计算应用，例如蒙特卡洛模拟和贝叶斯神经元网络，最终提出了一个框架。基于基于概率的计算技术。
The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.
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