Brain-like Machine: Thought and Architecture
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摘要: 经典计算机的理论边界在1936年就由图灵确定了，冯·诺依曼体系结构计算机也受限于图灵机模型.囿于神经形态器件的缺失，神经网络模型一直在经典计算机上运行.然而，冯·诺依曼体系结构与神经网络的异步并行结构及通信机制并不匹配，表现之一是功耗巨大，发展面向神经网络的体系结构，对于人工智能乃至一般意义上的信息处理都是重要方向.类脑机是仿照生物神经网络、采用神经形态器件构造的、以时空信息处理为特征的智能机器.类脑机的思想在计算机发明之前就提出了，研究开发实践也已经进行了30多年，多台类脑系统已经上线运行，其中SpiNNaker专注于类脑系统的体系结构研究，提出了一种行之有效的类脑方案.未来20年左右，预计模式动物大脑和人脑的精细解析将逐步完成，模拟生物神经元和神经突触信息处理功能的神经形态器件及集成工艺将逐步成熟，结构逼近大脑、性能远超大脑的类脑机有望实现.类脑机像生物大脑一样都是脉冲神经网络，神经形态器件具有真正的随机性，因此类脑机具备丰富的非线性动力学行为.已证明任何图灵机均可由脉冲神经网络构造出来，类脑机在理论上是否能够超越图灵机，是需要突破的一个重大问题.Abstract: The theoretical limitation of the classical computing machinery, including all the computers with von Neumann architecture, was defined by Alan Turing in 1936. Owing to lack of the hardware neuromorphic devices, neural networks have been implemented with computers to realize artificial intelligence for decades. However, the von Neumann architecture doesn’t match with the asynchronous parallel structure and communication mechanism of the neural networks, with consequences such as huge power consumption. To develop the neural network oriented architecture for artificial intelligence and common information processing is an important direction for architecture research. Brain-like machine is an intelligent machine which is constructed with neuromorphic devices according to the structure of biological neural network, and is better on spatio-temporal information processing than classic computer. The idea of brain-like machine had been proposed before the invention of computer. The research and development practice has been carried out for more than three decades. As one of the several brain-like systems being in operation, SpiNNaker focuses on the research on the architecture of brain-like systems with an effective brain-like scheme. In the next 20 years or so, it is expected that the detailed analysis of model animal brain and human brain will be completed step by step, and the neuromorphic devices and integrated processes will be gradually mature, and the brain-like machine with structure close to the brain and performance far beyond the brain is expected to be realized. As a kind of spiking neural networks, and with neuromorphic devices which behavior is true random, the brain-like machine can emerge abundant nonlinear dynamic behaviors. It had been proven that any Turing machine can be constructed with spiking neural network. Whether the brain-like machine can transcend the theoretical limitation of the Turing machine? This is a big open problem to break through.
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