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CacheStudio enables engineering teams to rapidly model, simulate, and optimize complex cache hierarchies and memory systems using real workload behavior, data-driven analysis, and chiplet-aware architecture exploration.
Data movement between compute and memory is now one of the biggest drivers of system performance, power, and scalability. As systems grow in compute elements, memory channels, cache levels, and chiplet partitions, cache and memory architecture decisions become increasingly workload-dependent and difficult to optimize manually.
CacheStudio provides a rapid, programmatic platform to define, simulate, and analyze complex cache hierarchies and memory systems. Through Python-based specification, fast simulation, and interactive reporting, teams can compare architecture options, identify bottlenecks, optimize cache sizing and topology, and make data-driven decisions earlier in the design flow.
Rapidly define complex cache levels, topology, address maps, cache parameters, and coherence domains through a programmatic Python-based environment.
Simulate real workloads with cache-state, bandwidth, and structural-latency accuracy to evaluate miss rates, snoop behavior, bandwidth demand, and performance impact.
Analyze results across multiple abstraction levels, including cache hierarchy views, chiplet topology, traffic subsets, latency, bandwidth, directory utilization, and MOESI state behavior.
Evaluate cache hierarchy and chiplet partitioning together, accounting for chiplet-to-chiplet bandwidth, latency, and crossing overheads.
Compare architecture options based on real workload behavior to optimize cache sizing, line size, snoop filter capacity, bandwidth provisioning, and memory system efficiency.
Discover how CacheStudio helps engineering teams model, simulate, and optimize cache hierarchies and memory systems using workload-driven analysis. Request a personalized demo or connect with our team to discuss your architecture goals.
Discover how CacheStudio supports workload-driven exploration of cache and memory hierarchies for chiplet-based systems. The brief outlines how users can define cache configurations and analyze miss rates, snoop rates, directory utilization, state transitions, bandwidth, and latency to guide decisions about cache sizing, line size, snoop-filter capacity, and partitioning across process nodes.