It's the principle that you can often reduce time cost by spending more memory, or reduce memory by spending more time, they trade against each other. The classic embedded example is a lookup table: instead of computing a function at runtime (say a sine value, a CRC step, or a gamma correction), you precompute the results into a table at build time and replace an expensive O(k) computation with an O(1) array read, paying flash/RAM for the table to buy speed and determinism. Hashing is another: you allocate a sparse table (memory) to turn an O(n) search into an O(1)-average lookup. The reverse direction also happens, on a RAM-starved part you might recompute a value each time rather than cache it. The embedded skill is placing yourself on that curve deliberately given your flash, RAM, and WCET budget; there's no universally right point.
Data Structures & Algorithms · Interview question
What is the space-time tradeoff, and give an embedded example.
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Time & Space Complexity
Big-O describes how cost grows with input size, and on an MCU you also care about the constants it hides, worst-case determinism (WCET), and the space-time tradeoff.