Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approximation in machine learning algorithms suitable for mixed-signal Analog In-Memory Computing (AIMC) architectures. Analog In-Memory Kernel Approximation addresses the performance bottlenecks of conventional kernel-based methods by executing most operations in approximate kernel methods directly in memory. The IBM HERMES Project Chip, a state-of-the-art phase-change memory based AIMC chip, is utilized for the hardware demonstration of kernel approximation. Experimental results show that our method maintains high accuracy, with less than a 1% drop in kernel-based ridge classification benchmarks and within 1% accuracy on the Long Range Arena benchmark for kernelized attention in Transformer neural networks. Compared to traditional digital accelerators, our approach is estimated to deliver superior energy efficiency and lower power consumption. These findings highlight the potential of heterogeneous AIMC architectures to enhance the efficiency and scalability of machine learning applications.

翻译：核心函数是多种机器学习算法的关键组成部分，但通常带来显著的内存与计算开销。本文提出一种适用于混合信号模拟存内计算架构的机器学习核心函数近似方法。模拟存内核心函数近似通过将近似核心方法中的大部分操作直接在存储器内执行，解决了传统基于核心方法的性能瓶颈。研究采用基于相变存储器的先进模拟存内计算芯片——IBM HERMES项目芯片，进行了核心函数近似的硬件验证。实验结果表明，该方法在保持高精度的同时，在基于核心的岭分类基准测试中精度下降小于1%，在Transformer神经网络中核心化注意力机制的Long Range Arena基准测试中精度保持在1%误差范围内。与传统数字加速器相比，本方法预计能实现更优的能效与更低的功耗。这些发现凸显了异构模拟存内计算架构在提升机器学习应用效率与可扩展性方面的潜力。