Recent development and progress of Artificial Intelligence (AI) algorithms made clear that this topic is a paradigm shift with respect to the past. High throughput and ability to do complex tasks makes AI a great field of opportunity. This advancement is somehow limited by the physical implementation of the chips that are still bound to the historical von-Neumann Architecture with processing units and memory hardware spatially separated. The way data is bussed and processed needs disruptive innovation, rather than an evolutionary approach, too. In Analog In-Memory Computing (A-IMC) the typical properties of resistance-based memory technologies are used to both store and compute information. This allows for incredibly high parallelism and removes the problems related to the known von-Neumann bottleneck. In the present work, A-IMC networks based on resistive memories and on the Phase Change Memory (PCM) technology, in particular, are extensively discussed. After a first review of the general features of PCM devices, their application to A-IMC is described, aiming at a full description of the current technological scenario.

中文翻译：

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相变存储器：模拟内存计算 （A-IMC） 应用中的电气行为和应用综述


人工智能 （AI） 算法的最新发展和进展清楚地表明，这个话题是相对于过去的范式转变。高吞吐量和执行复杂任务的能力使 AI 成为一个巨大的机会领域。这种进步在某种程度上受到芯片物理实现的限制，这些芯片仍然与历史悠久的 von-Neumann 架构绑定，处理单元和内存硬件在空间上是分开的。数据的总线和处理方式也需要颠覆性创新，而不是进化的方法。在模拟内存计算 （A-IMC） 中，基于电阻的内存技术的典型特性用于存储和计算信息。这允许令人难以置信的高并行性，并消除了与已知的 von-Neumann 瓶颈相关的问题。在本研究中，特别广泛讨论了基于电阻存储器和相变存储器 （PCM） 技术的 A-IMC 网络。在首先回顾了 PCM 设备的一般特性之后，描述了它们在 A-IMC 中的应用，旨在全面描述当前的技术场景。