The precise programming of crossbar arrays of unit-cells is crucial for obtaining high matrix-vector-multiplication (MVM) accuracy in analog in-memory computing (AIMC) cores. We propose a radically different approach based on directly minimizing the MVM error using gradient descent with synthetic random input data. Our method significantly reduces the MVM error compared with conventional unit-cell by unit-cell iterative programming. It also eliminates the need for high-resolution analog-to-digital converters (ADCs) to read the small unit-cell conductance during programming. Our method improves the experimental inference accuracy of ResNet-9 implemented on two phase-change memory (PCM)-based AIMC cores by 1.26%.

翻译：单元阵列的精确编程对于在模拟存内计算核心中实现高精度矩阵向量乘法至关重要。我们提出了一种根本不同的方法，通过使用梯度下降和合成随机输入数据直接最小化矩阵向量乘法误差。与传统的逐单元迭代编程相比，我们的方法显著降低了矩阵向量乘法误差，同时消除了在编程过程中读取微小单元电导所需的高分辨率模数转换器。该方法将基于两个相变存储器的模拟存内计算核心上实现的ResNet-9的实验推理精度提升了1.26%。