VisualSim is a system-level architecture exploration tool for:
ARM-based SoCs
Cortex-A/R/M, Neoverse, Mali GPUs, AMBA buses, CoreLink/CMN interconnects.
Non-ARM SoCs
RISC-V, PowerPC, Tensilica DSP, TI DSP, Synopsys ARC.
Custom/Domain-Specific SoCs
PCIe switches, Infotainment, Autonomous vehicles, CXL memory expanders, AI accelerators, networking ASICs.
Engineers can model end-to-end SoC behavior — CPUs, accelerators, cache hierarchies, NoCs, memory controllers, DDR/LPDDR/GDDR/HBM, and chiplet interconnects — to optimize latency, throughput, bandwidth, power, and area trade-offs.
Compare core counts, cache levels, and interconnect topologies in days, not months.
Measure throughput, latency, utilization, and energy at system level.
Evaluate disaggregated systems with real die-to-die traffic.
Distribute encrypted models to OEMs for integration without exposing IP.
Co-simulate RF, mechnical, thermal, power and digital for realistic validation.
Faster Time-to-Market
Catch performance bottlenecks and reduce power consumption before RTL.
Lower Risk
Avoid late-stage redesigns caused by unverified assumptions.
Customer Co-Design
Engage OEMs early with usable encrypted models.
Optimized Cost & Power
Data-driven component selection to meet Power-Performance-Area (PPA) goals.
Chiplet & UCIe Modeling
Simulate heterogeneous chiplets and traffic across dies.
Flexible NoC/Interconnect Support
Mesh, ring, hierarchical, or custom topologies. SUpport commercial vendor and custom technology
Memory System Design
LPDDR5-X, DDR5, GDDR6, HBM2/3 with real controller behavior.
Parameterized Libraries
Ready-to-use CPUs, GPUs, DSPs, NPUs, controllers, and buses from most of the major IP vendors.
Encrypted Model Sharing
Provide OEMs with reference SoC models while protecting IP.
Networking SoC Vendor
Cut interconnect evaluation time by 70%.
AI Accelerator Startup
Boosted inference throughput by 30% through early architecture tuning.
Chiplet Processor Company
Avoided costly redesign by modeling UCIe latency and power upfront.
Evaluate cluster performance for AI inference.
Balance CPU/DSP/NPU resources for automotive workloads.
Optimize routing, QoS, arbitration, buffering, and packet delay.
Optimize memory assignment, distribution of tasks and I/O chiplets to meet PPA.
Analyze cache coherence and bandwidth scalability.
