A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems
A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems
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具有相变记忆神经元和突触的尖峰循环神经网络，用于加速解决约束满足问题
DOI：
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发表时间：
2020
影响因子：
2.4
通讯作者：
D. Ielmini
中科院分区：
文献类型：
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作者：
G. Pedretti;P. Mannocci;S. Hashemkhani;V. Milo;O. Melnic;E. Chicca;D. Ielmini
数据密集型计算应用，如对象识别、时间序列预测和优化任务，在智能移动、健康和工业等多个领域变得越来越重要。由于计算中涉及大量数据，传统的冯诺依曼架构由于存储器瓶颈而遭受过度的延迟和能量消耗。更有效的方法包括内存计算（IMC），其中计算操作直接在数据中执行。IMC可以利用存储器设备的丰富物理特性，例如它们存储要在矩阵向量乘法（MVM）中使用的模拟值的能力以及它们在优化和约束满足问题（CSP）的框架中非常有价值的随机性。本文提出了一种基于相变记忆（PCM）器件的随机发放神经元，用于Hopfield递归神经网络（RNN）中的CSP解决方案。在RNN中，PCM单元被用作随机神经元的积分单元，支持典型CSP的解决方案，即硬件数独问题。最后，使用基于PCM的神经元的RNN解决数独谜题的能力进行了研究，通过一个紧凑的仿真模型来增加数独谜题的大小，从而支持我们的基于PCM的RNN用于数据密集型计算。
Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix–vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.
DOI：
10.1145/3357526.3357566
发表时间：
2019-09
期刊：
Proceedings of the International Symposium on Memory Systems
影响因子：
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作者：
Xiaochen Peng;Minkyu Kim;Xiaoyu Sun;Shihui Yin;Titash Rakshit;R. Hatcher;J. Kittl;Jae-sun Seo;Shimeng Yu
通讯作者：
Xiaochen Peng;Minkyu Kim;Xiaoyu Sun;Shihui Yin;Titash Rakshit;R. Hatcher;J. Kittl;Jae-sun Seo;Shimeng Yu