Probabilistic Circuits for Autonomous Learning: A Simulation Study
Probabilistic Circuits for Autonomous Learning: A Simulation Study
复制标题
用于自主学习的概率电路：仿真研究
DOI：
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发表时间：
2019
影响因子：
3.2
通讯作者：
S. Datta
中科院分区：
文献类型：
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作者：
J. Kaiser;Rafatul Faria;Kerem Y Çamsarı;S. Datta
现代机器学习基于在数字计算平台上运行的强大算法，人们对加速学习过程并使其更节能非常感兴趣。在本文中，我们提出了一种完全自主的概率电路，用于快速高效学习，且不使用数字计算。具体而言，我们使用SPICE模拟来展示一种无时钟的自主电路，其中所需的突触权重以模拟电压的形式读出。这使我们能够展示一种可以用现有技术构建的电路，以模拟基于最大似然函数梯度优化的玻尔兹曼机器学习算法。在移动和边缘计算的背景下，这种自主电路作为独立的学习设备可能会特别令人感兴趣。
Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous circuit where the required synaptic weights are read out in the form of analog voltages. This allows us to demonstrate a circuit that can be built with existing technology to emulate the Boltzmann machine learning algorithm based on gradient optimization of the maximum likelihood function. Such autonomous circuits could be particularly of interest as standalone learning devices in the context of mobile and edge computing.
影响因子：
1.2
作者：
Hassan, Orchi;Faria, Rafatul;Datta, Supriyo
通讯作者：
Datta, Supriyo