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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.
Capabilities
01: CAPTURE
Define complex cache and memory systems in minutes using an intuitive, programmable Python API. Capture hierarchy, capacities, policies, address maps, and component parameters in reusable models built for rapid exploration.
API-based system specification
Parameterized, reusable design models
Automated sweeps and custom scoring
02: EXPLORE
Build any logical cache hierarchy, then refine it into an address-sliced physical design. Configure coherent and non-coherent caches, directories, memories, and policies without tying the system model to a single coherency protocol.
Logical and physical hierarchy views
Protocol-neutral coherency modeling
Flexible cache, directory, and address-map controls
03: SIMULATE
Drive every leaf cache with memory or CHI traces that reflect the workloads your system will run. CacheStudio models hits, misses, refills, writebacks, and snoops as they move through the hierarchy, turning representative inputs into architecture-level behavior.
Memory and CHI trace support
Independent stimulus for each leaf node
Multi-workload, multi-design sweeps
04: ANALYZE
Run long, stateful simulations at millions of instructions per second while preserving cache-line and directory state. Review results at logical, physical, and chiplet levels, isolate specific traffic flows, and compare design candidates using metrics defined for your system.
Hit rates, miss causes, occupancy, and line-state data
Latency, peak and sustained bandwidth, and D2D utilization
Interactive filtering, time-series analysis, and custom KPIs
05: OPTIMIZE
Assign cache, memory, and I/O nodes to chiplets, define traffic routes, and model each die-to-die boundary. Compare organizations using workload-derived bandwidth and latency demands before committing to an architecture.
Flexible node-to-chiplet optimization
Direct and multi-hop die-to-die routing
Cache hierarchy and chiplet co-optimization
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.
Products
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.