In analog neuromorphic chips, designers can embed computing primitives in the intrinsic physical properties of devices and circuits, heavily reducing device count and energy consumption, and enabling high parallelism, because all devices are computing simultaneously. Neural network parameters can be stored in local analog non-volatile memories (NVMs), saving the energy required to move data between memory and logic. However, the main drawback of analog sub-threshold electronic circuits is their dramatic temperature sensitivity. In this paper, we demonstrate that a temperature compensation mechanism can be devised to solve this problem. We have designed and fabricated a chip implementing a two-layer analog neural network trained to classify low-resolution images of handwritten digits with a low-cost single-poly complementary metal-oxide-semiconductor (CMOS) process, using unconventional analog NVMs for weight storage. We demonstrate a temperature-resilient analog neuromorphic chip for image recognition operating between 10$^{\circ}$C and 60$^{\circ}$C without loss of classification accuracy, within 2\% of the corresponding software-based neural network in the whole temperature range.

翻译：在模拟神经形态芯片中，设计者可将计算原语嵌入到器件与电路固有的物理特性中，从而大幅减少器件数量并降低能耗，同时实现高度并行计算，因为所有器件均在同步执行运算。神经网络参数可存储于本地模拟非易失性存储器中，从而节省了数据在存储与逻辑单元间传输所需的能量。然而，模拟亚阈值电子电路的主要缺陷在于其强烈的温度敏感性。本文论证了可通过设计温度补偿机制解决该问题。我们设计并制造了一款芯片，该芯片采用低成本单层多晶硅互补金属氧化物半导体工艺实现了一个双层模拟神经网络，并利用非常规模拟非易失性存储器进行权重存储，该网络经训练可对手写数字低分辨率图像进行分类。实验证明，这款用于图像识别的模拟神经形态芯片在10$^{\circ}$C至60$^{\circ}$C的温度范围内均能保持分类精度，其性能在整个温度区间内与对应软件神经网络相比误差不超过2%，展现出优异的温度鲁棒性。