大多数 CPU 仍然基于 Von-Neumann 架构(或更准确地说,存储程序计算机),其中数据从内存带到处理器,进行操作,然后写回内存。 基于 Von-Neumann 的标量和矢量处理器架构在控制流方面表现出色,但难以保证确定性。这就是 FPGA 和 ASIC 作为机器人系统的关键支持技术发挥作用的地方。 The Von-Neumann based architectures of scalar and vector processors excel at control flow, but struggle
accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" problem associated with von-Neumann
Using the von-Neumann entanglement entropy (EE) as a proxy for information propagation, we show that
Using the von-Neumann entanglement entropy (EE) as a proxy for information propagation, we show that