Chair of Highly-Parallel VLSI-Systems and Neuromorphic Circuits, Technische Universität Dresden Dresden, Germany.
Front Neurosci. 2011 Oct 12;5:117. doi: 10.3389/fnins.2011.00117. eCollection 2011.
State-of-the-art large-scale neuromorphic systems require sophisticated spike event communication between units of the neural network. We present a high-speed communication infrastructure for a waferscale neuromorphic system, based on application-specific neuromorphic communication ICs in an field programmable gate arrays (FPGA)-maintained environment. The ICs implement configurable axonal delays, as required for certain types of dynamic processing or for emulating spike-based learning among distant cortical areas. Measurements are presented which show the efficacy of these delays in influencing behavior of neuromorphic benchmarks. The specialized, dedicated address-event-representation communication in most current systems requires separate, low-bandwidth configuration channels. In contrast, the configuration of the waferscale neuromorphic system is also handled by the digital packet-based pulse channel, which transmits configuration data at the full bandwidth otherwise used for pulse transmission. The overall so-called pulse communication subgroup (ICs and FPGA) delivers a factor 25-50 more event transmission rate than other current neuromorphic communication infrastructures.
最先进的大规模神经形态系统需要在神经网络的单元之间进行复杂的尖峰事件通信。我们提出了一种基于特定于应用的神经形态通信集成电路(在现场可编程门阵列(FPGA)维护的环境中)的晶圆级神经形态系统的高速通信基础设施。这些集成电路实现了可配置的轴突延迟,这是某些类型的动态处理或模拟远距离皮质区域之间基于尖峰的学习所必需的。我们提出了测量结果,这些结果表明这些延迟在影响神经形态基准测试的行为方面的有效性。在大多数当前系统中,专用的、专用的地址事件表示通信需要单独的、低带宽配置通道。相比之下,晶圆级神经形态系统的配置也由基于数字分组的脉冲通道处理,该通道以脉冲传输的全带宽传输配置数据。总体上所谓的脉冲通信子组(集成电路和现场可编程门阵列)比其他当前的神经形态通信基础设施提供了 25-50 倍以上的事件传输速率。