无需复杂工艺，芯片在-10℃至60℃间稳定识别手写数字。
Temperature-Resilient Analog Neuromorphic Chip in Single-Polysilicon CMOS Technology
- 用单多晶硅CMOS工艺实现温度自补偿的类脑模拟电路
- 在-10℃到60℃范围内分类准确率与软件模型相差小于2%
- 适合低功耗、高稳定性边缘计算场景
在模拟类脑芯片中，设计者可将计算单元嵌入器件和电路的物理特性中，大幅减少器件数量与能耗，并实现高并行性，因所有器件同时进行计算。神经网络参数可存储于本地模拟非易失性存储器（NVM）中，避免数据在内存与逻辑间搬运的能耗。然而，模拟亚阈值电路的主要缺点是温度敏感性强。本文提出一种温度补偿机制解决该问题。我们设计并流片了一枚芯片，采用低成本单多晶硅互补金属氧化物半导体（CMOS）工艺，实现两层模拟神经网络，用于识别低分辨率手写数字图像，使用非传统模拟NVM存储权重。实验表明，该芯片在10℃至60℃温度范围内运行时，图像识别准确率无下降，与对应软件神经网络相比误差不超过2%。
原文摘要 · Abstract (English)
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.
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