Numerical computation is essential to many areas of artificial intelligence (AI), whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized by the slowdown in Moore's law. Multi-function multi-way analog (MFMWA) technology, a computing architecture comprising arrays of memristors supporting in-memory computation of matrix operations, can offer tremendous improvements in computation and energy, but at the expense of inherent unpredictability and noise. We devise novel randomized algorithms tailored to MFMWA architectures that mitigate the detrimental impact of imperfect analog computations while realizing their potential benefits across various areas of AI, such as applications in computer vision. Through analysis, measurements from analog devices, and simulations of larger systems, we demonstrate orders of magnitude reduction in both computation and energy with accuracy similar to digital computers.

翻译：数值计算是人工智能（AI）众多领域的基础，其计算需求持续快速增长，然而摩尔定律的放缓正危及这些需求的持续扩展。多功能多路模拟（MFMWA）技术是一种由忆阻器阵列构成的计算架构，支持矩阵运算的内存计算，能够在计算和能耗方面带来巨大改进，但代价是固有的不可预测性和噪声。我们针对MFMWA架构设计了新颖的随机化算法，在减轻不完美模拟计算的有害影响的同时，发挥其在不同AI领域（如计算机视觉应用）的潜在优势。通过分析、模拟器件测量以及更大系统的仿真，我们展示了在计算和能耗上实现数量级降低的同时，精度与数字计算机相当。