Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
Kyungbook National University, Daegu, Republic of Korea.
Sci Adv. 2023 Jul 21;9(29):eadg9123. doi: 10.1126/sciadv.adg9123. Epub 2023 Jul 19.
Neuromorphic computing (NC) architecture inspired by biological nervous systems has been actively studied to overcome the limitations of conventional von Neumann architectures. In this work, we propose a reconfigurable NC block using a flash-type synapse array, emerging positive feedback (PF) neuron devices, and CMOS peripheral circuits, and integrate them on the same substrate to experimentally demonstrate the operations of the proposed NC block. Conductance modulation in the flash memory enables the NC block to be easily calibrated for output signals. In addition, the proposed NC block uses a reduced number of devices for analog-to-digital conversions due to the super-steep switching characteristics of the PF neuron device, substantially reducing the area overhead of NC block. Our NC block shows high energy efficiency (37.9 TOPS/W) with high accuracy for CIFAR-10 image classification (91.80%), outperforming prior works. This work shows the high engineering potential of integrating synapses and neurons in terms of system efficiency and high performance.
受生物神经系统启发的神经形态计算 (NC) 架构被积极研究以克服传统冯·诺依曼架构的局限性。在这项工作中，我们提出了一种使用闪存型突触阵列、新兴的正反馈 (PF) 神经元器件和 CMOS 外围电路的可重构 NC 块，并将它们集成在同一衬底上，以实验验证所提出的 NC 块的操作。闪存中的电导调制使 NC 块能够轻松地对输出信号进行校准。此外，由于 PF 神经元器件的超陡开关特性，所提出的 NC 块使用较少的器件进行模数转换，从而大大减少了 NC 块的面积开销。我们的 NC 块在 CIFAR-10 图像分类方面表现出高准确性（91.80%）和高能量效率（37.9TOPS/W），优于先前的工作。这项工作展示了在系统效率和高性能方面集成突触和神经元的高工程潜力。
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