You can never have enough memory for an electronic design, especially if your end product involves artificial intelligence (AI) or machine learning (ML). Large amounts of high-performing memory deliver the real-time (or near real-time) results that systems such as autonomous driving and smart devices require.
In the interest of meeting specific performance, power, and area (PPA) needs for AI, servers, automotive, and the like, the design world is moving toward more customized chips rather than general-purpose memory devices. Given the increasing prevalence of these data-intensive applications, chip designers need to quickly produce derivative designs and variants to satisfy the demands.
How can you meet time-to-market targets while developing increasingly large and complex memory devices that satisfy aggressive PPA goals?
This blog post, adapted from a previously published Semiconductor Engineering article, explains why traditional memory design flows are inadequate to support advanced memory devices. Read on to learn how shifting left, with a big assist from machine learning, can help you accelerate your memory design cycle.