> For the complete documentation index, see [llms.txt](https://elishai.gitbook.io/neuromorphic-engineering/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://elishai.gitbook.io/neuromorphic-engineering/architect-perspective/10.-in-memory-computing-with-memristors.md).

# 10. In-memory computing with memristors

Chapter 10

The memristor, “the fourth element,” is a prominent candidate for neuromorphic non-CMOS hardware. Memristors have been a subject of interest for mathematicians, physicists, material scientists, electrical engineers, and neuroscientists. Memristors’ physical implementation is also broad and diverse. Memristors were proposed to have a key rule in future deep learning accelerators and, due to their synapse-like plasticity and neuron-like characteristics, they can have an important impact on SNNs. In this chapter, we will discuss the basics of in-memory computing with memristors. This chapter does not aim to give a formal and rigorous description of the memristor but rather provide some intuition regarding its potential rule in neuromorphic engineering.

![](https://1487620879-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MiS7NytljlKs-2Yccqw%2F-Mj4R7A4YdDW-WV8q_h6%2F-Mj4XSKDJDqQPvNLdVt1%2FScreen%20Shot%202021-09-08%20at%2016.12.24.png?alt=media\&token=746085f8-6fe6-4951-8e60-f9f842465675)