Intelligent Metrics for Intelligent Computing
Silicon iQ™ is Cornami’s industry-first performance analysis framework that redefines how semiconductor architectures are evaluated.
In a world where traditional benchmarks fall short—measuring narrow performance traits in isolated conditions—Silicon iQ™ takes a broader, application-relevant approach.
- It assesses what truly matters: decision throughput, energy efficiency, transistor-level optimization, and architectural scalability.
- These are the metrics that define success in AI, machine learning, secure computing, and edge-to-cloud workloads.
Silicon iQ™ doesn’t just measure performance—it reveals architectural intelligence.
Architectural Scalability
Measuring Performance from Edge to Exascale
Many processors perform well under narrow conditions. Few scale. Architectural Scalability is the measure of how well a system grows with the workload, the data size, and the deployment environment.
Silicon iQ™ evaluates scalability across use cases—from embedded inference at the edge to multi-tenant AI pipelines in the cloud. It looks at parallel execution, dataflow elasticity, and software-to-hardware orchestration to quantify true adaptability.
- One size doesn’t fit all.
- Scalable architectures reduce total cost of ownership, accelerate time to value, and future-proof investments by handling today’s workloads and tomorrow’s unknowns—without a rewrite.
Simultaneous Decisions per Clock Cycle
Unleashing Parallelism Where It Matters Most
Traditional compute architectures process one instruction stream at a time, gating throughput and leaving performance untapped.
Silicon iQ™ highlights what really drives performance at scale: the number of independent, data-driven decisions that can be made in every clock cycle.
This metric directly reflects the core architectural parallelism and is foundational for high-efficiency AI inference, FHE acceleration, and real-time edge analytics.
The more decisions per cycle, the more work gets done, without the latency, overhead, or synchronization penalties that plague conventional processors.
- Modern applications are decision-dense. Video analytics, encrypted data workflows, and LLM inference require millions of simultaneous micro-decisions.
- Silicon iQ™ makes this measurable—and optimizable.
TOP/s per Watt
Real-world Throughput Efficiency vs. Performance in Isolation
Traditional compute architectures process one instruction stream at a time, gating throughput and leaving performance untapped.
Silicon iQ™ highlights what really drives performance at scale: the number of independent, data-driven decisions that can be made in every clock cycle.
This metric directly reflects the core architectural parallelism and is foundational for high-efficiency AI inference, FHE acceleration, and real-time edge analytics.
The more decisions per cycle, the more work gets done—without the latency, overhead, or synchronization penalties that plague conventional processors.
- In edge and embedded deployments, energy is the constraint. In datacenters, power is the cost center.
- Systems with high TOPS/Watt are not just faster—they’re viable, scalable, and cost-effective.
GOP/s per Million Transitions
Quantifying Compute Efficiency at the Silicon Level
This metric evaluates how effectively a processor delivers computational work relative to transistor activity—capturing the relationship between logical operations and switching events. GOP/s per Million Transitions helps reveal how much useful performance is being extracted from the silicon without excessive energy loss or data movement overhead.
Rather than emphasizing raw throughput alone, this efficiency-focused measure offers insight into how well a design minimizes waste in switching, signaling, and internal resource utilization.
- In AI, ML, and encrypted workloads, where dataflow, memory access, and control logic can dominate power budgets, this metric highlights the architectures that perform efficiently without driving up switching costs. It helps identify balanced designs that do more with less.