Start by clarifying requirements and constraints, such as cache size, associativity, replacement policy, and performance goals. Then outline a high-level design, diving into key components like tag store, data store, and control logic, while discussing trade-offs and edge cases.
Pro tip: Demonstrate awareness of real-world constraints like power consumption, area, and timing, and relate them to software implications such as cache coherence and consistency models.
Ask questions to understand the scope: cache size, block size, associativity, write policy, replacement policy, and performance targets. Confirm whether it's for a CPU, GPU, or distributed system.
Sketch the main components: tag array, data array, valid/dirty bits, replacement logic, and interface with memory. Explain how a request flows through the cache.
Discuss design choices for each component, such as direct-mapped vs. set-associative, LRU vs. random replacement, write-through vs. write-back. Analyze trade-offs in terms of hit rate, latency, and complexity.
Address scenarios like cache misses, evictions, coherence in multi-core systems, and optimizations like prefetching or multi-level caches.
Recap the design, highlight key decisions, and suggest how to evaluate performance (e.g., simulation, metrics like AMAT).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.