论文标题
评估新兴记忆推理机中的复杂性和弹性权衡
Evaluating complexity and resilience trade-offs in emerging memory inference machines
论文作者
论文摘要
神经形态式的推断只有在适当地最大化有限的硬件资源时才效果很好，例如面对潜在干扰，准确性继续随参数和复杂性而扩展。在这项工作中，我们使用逼真的横梁模拟来强调，深层神经网络的紧凑实现意外容易因多种系统干扰而崩溃。我们的工作提出了使用马赛克框架的高性能和强大弹性的中间路，尤其是通过在具有自然形式的噪声免疫形式的复发性神经网络实现中重复使用突触连接。
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of potential disturbance. In this work, we use realistic crossbar simulations to highlight that compact implementations of deep neural networks are unexpectedly susceptible to collapse from multiple system disturbances. Our work proposes a middle path towards high performance and strong resilience utilizing the Mosaics framework, and specifically by re-using synaptic connections in a recurrent neural network implementation that possesses a natural form of noise-immunity.
