论文标题
RANC：神经形态计算的可重构体系结构
RANC: Reconfigurable Architecture for Neuromorphic Computing
论文作者
论文摘要
已引入神经形态架构作为节能尖峰神经网络执行的平台。这些体系结构提供的大量并行性也引起了非机器学习应用程序领域的兴趣。为了提高式硬件设计人员和应用程序开发人员的进入障碍，我们提出了RANC：一种可重新配置的神经形态计算的架构，神经形态计算是一个开源的高度灵活的生态系统，可以通过C ++模拟和硬件通过FPGA模拟来快速实验神经形态架构。我们通过展示其重现IBM Truenorth行为并与IBM的指南针模拟环境和已发表文献进行直接比较的能力来介绍Ranc生态系统的实用性。 RANC允许根据应用程序见解以及原型的未来神经形态体系结构进行优化，这些架构可以完全支持新的应用程序。我们通过研究建筑变化对提高应用程序映射效率的影响通过基于ALVEO U250 FPGA的定量分析来证明RANC的高度参数化和可配置的性质。我们介绍了跨合成孔径雷达分类和向量矩阵乘法应用程序实现的后路由资源使用和吞吐量分析，并演示了一种神经形态体系结构，该神经形态结构缩放到模拟259K不同的神经元和73.3m不同的突触。
Neuromorphic architectures have been introduced as platforms for energy efficient spiking neural network execution. The massive parallelism offered by these architectures has also triggered interest from non-machine learning application domains. In order to lift the barriers to entry for hardware designers and application developers we present RANC: a Reconfigurable Architecture for Neuromorphic Computing, an open-source highly flexible ecosystem that enables rapid experimentation with neuromorphic architectures in both software via C++ simulation and hardware via FPGA emulation. We present the utility of the RANC ecosystem by showing its ability to recreate behavior of the IBM's TrueNorth and validate with direct comparison to IBM's Compass simulation environment and published literature. RANC allows optimizing architectures based on application insights as well as prototyping future neuromorphic architectures that can support new classes of applications entirely. We demonstrate the highly parameterized and configurable nature of RANC by studying the impact of architectural changes on improving application mapping efficiency with quantitative analysis based on Alveo U250 FPGA. We present post routing resource usage and throughput analysis across implementations of Synthetic Aperture Radar classification and Vector Matrix Multiplication applications, and demonstrate a neuromorphic architecture that scales to emulating 259K distinct neurons and 73.3M distinct synapses.
