论文标题
拥抱用于神经形态计算的内存设备的不可靠性
Embracing the Unreliability of Memory Devices for Neuromorphic Computing
论文作者
论文摘要
电阻非易失性记忆的出现为近内存或内存的高能计算开辟了道路。但是，这种类型的计算与常规ECC不兼容，并且必须处理设备不可靠。受动物大脑结构的启发，我们提出了一个制造的差异混合CMOS/RRAM内存架构，适用于无正式ECC功能的神经网络实现。我们还表明，使用低能但容易出错的编程条件仅稍微降低网络精度。
The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with conventional ECC, and has to deal with device unreliability. Inspired by the architecture of animal brains, we present a manufactured differential hybrid CMOS/RRAM memory architecture suitable for neural network implementation that functions without formal ECC. We also show that using low-energy but error-prone programming conditions only slightly reduces network accuracy.
