  As the relative power, performance, and area (PPA) impact of embedded
memories continues to grow, proper parameterization of each of the thousands of
memories on a chip is essential. When the parameters of all memories of a
product are optimized together as part of a single system, better trade-offs
may be achieved than if the same memories were optimized in isolation. However,
challenges such as a sparse solution space, conflicting objectives, and
computationally expensive PPA estimation impede the application of common
optimization heuristics. We show how the memory system optimization problem can
be solved through computational intelligence. We apply a Pareto-based
Differential Evolution to ensure unbiased optimization of multiple PPA
objectives. To ensure efficient exploration of a sparse solution space, we
repair individuals to yield feasible parameterizations. PPA is estimated
efficiently in large batches by pre-trained regression neural networks. Our
framework enables the system optimization of thousands of memories while
keeping a small resource footprint. Evaluating our method on a tractable
system, we find that our method finds diverse solutions which exhibit less than
0.5% distance from known global optima.
