Memory and Information Processing in Neuromorphic Systems
Memory and Information Processing in Neuromorphic Systems
复制标题
DOI：
10.1109/jproc.2015.2444094
复制
发表时间：
2015-08-01
影响因子：
20.6
通讯作者：
Liu, Shih-Chii
中科院分区：
文献类型：
--
作者：
Indiveri, Giacomo;Liu, Shih-Chii
大脑启发的神经形态处理器和当前的冯·诺依曼处理器架构之间的一个显着差异是记忆和处理的组织方式。随着信息和通信技术继续通过增加数字处理器内的内核来解决对增加计算能力的需求，神经形态工程师和科学家可以通过构建存储器与处理一起分布的处理器架构来补充这一需求。在本文中，我们对支持皮层网络和深度神经网络模型的大脑启发处理器架构进行了调查。这些架构的范围从多神经元系统的串行时钟实现到大规模并行异步系统，从纯数字系统到混合模拟/数字系统，这些系统实现了更类似生物的神经元和突触模型，以及一套类似于生物神经系统中发现的适应和学习机制。我们描述了所追求的不同方法的优点，并提出了需要解决的挑战，以建立人工神经处理系统，可以显示丰富的生物系统中看到的行为。
A striking difference between brain-inspired neuromorphic processors and current von Neumann processor architectures is the way in which memory and processing is organized. As information and communication technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper, we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multineuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.