Shin Yunwoo, Cho Kyoungah, Kim Sangsig
Department of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Sci Rep. 2024 Mar 11;14(1):5891. doi: 10.1038/s41598-024-56575-4.
In this study, a binarized neural network (BNN) of silicon diode arrays achieved vector-matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p-n-p-n device structure possess steep switching and bistable characteristics with an extremely low subthreshold swing (below 1 mV) and a high current ratio (approximately 10). Moreover, the arrays show a self-rectifying functionality and an outstanding linearity by an R-squared value of 0.99986, which allows to compose a synaptic cell with a single diode. A 2 × 2 diode array can perform matrix multiply-accumulate operations for various binarized weight matrix cases with some input vectors, which is in high concordance with the VMM, owing to the high reliability and uniformity of the diodes. Moreover, the disturbance-free, nondestructive readout, and semi-permanent holding characteristics of the diode arrays support the feasibility of implementing the BNN.
在本研究中，硅二极管阵列的二值化神经网络（BNN）实现了这些阵列中二值化权重与输入之间的向量矩阵乘法（VMM）。在其p-n-p-n器件结构中以正反馈回路运行的二极管具有陡峭的开关和双稳态特性，亚阈值摆幅极低（低于1 mV）且电流比很高（约为10）。此外，这些阵列通过R平方值0.99986展现出自整流功能和出色的线性度，这使得可以用单个二极管组成一个突触单元。一个2×2二极管阵列可以对各种二值化权重矩阵情况与一些输入向量执行矩阵乘积累加运算，由于二极管的高可靠性和均匀性，这与向量矩阵乘法高度一致。此外，二极管阵列的无干扰、无损读出和半永久性保持特性支持了实现二值化神经网络的可行性。
