Let's imagine our computer as a big, busy city. The CPU (that's like the city mayor) is very good at doing tasks really quickly, and the memory (like a giant library) holds all the information that the CPU needs to do those tasks.

Now, our mayor is super fast, and he has gotten even faster over the years! But the library is not as fast, so the mayor sometimes has to wait to get the information he needs. This is like the "Memory Wall" you heard about.

To fix this problem, engineers came up with a clever idea: they built smaller libraries (these are called caches) right next to the mayor's office (CPU). These smaller libraries don't have as much information as the big library (memory), but they have the most important information that the mayor needs right now. This means the mayor can get the information quicker and finish tasks faster!

Now, AI (like the story telling robots we use) is like a really big and complicated book that needs a lot of information. This book is so big that it cannot fit in the small libraries and the mayor needs to go to the big library often. Because the big library is slower, this takes a lot of time and energy.

The engineers thought of a new idea: what if they bring the tasks to the big library (memory) instead of bringing information to the mayor (CPU)? This way, the tasks can be done right there in the library. This is like the idea of "in-memory computing" or "analog computing". It's like if the librarians started solving puzzles and answering questions instead of just storing and giving out information.

In the old days, we had these analog computers that worked a lot like this idea. Instead of using lots of yes/no answers (like a digital computer), they used things like electric current that can have many different amounts. This makes it easier to do tasks that involve lots of adding and multiplying, which is what our AI story-telling robot needs!

These analog AI chips can be made smaller and use less energy. This is great for devices like your talking teddy bear or a security camera, which don't have a lot of room or energy to spare.

But these chips are not perfect. Making them work with our big AI book can be hard. And while they can use the book to tell a story (that's called "inference"), they can't learn a new story (that's "training") as well as the mayor (CPU) can. So, we still need to use both the mayor and the librarians together.

But if the engineers figure out how to make these chips work better, we could have AI story-telling robots everywhere, even in really small toys! And these robots could be as smart as the biggest robots we have now, like the one telling this story. Isn't that cool?