How formal MDV can eliminate IP integration uncertainty
The increased deployment of silicon intellectual property (IP) blocks is vital to boosting productivity in the development of large, complex system-on-chip (SoC) designs. But the increase in SoC design productivity is not matched by as great an increase in SoC verification productivity. Managers and engineers still struggle with a persistent “verification productivity gap.” Why? Because there is a persistent IP verification quality gap, too. The resulting uncertainty about the original verification quality of individual IP blocks often requires time-consuming remedial verification by the SoC design team. The alternative is to risk SoC design failure because of inadequate IP verification, which ultimately delays the project even more.
This article outlines how the latest formal metric-driven verification (MDV) methodology and technologies can eliminate integration uncertainty through the automatic generation of Accellera-defined coverage metrics, without the assistance of simulation. This formal MDV methodology measures not only the usual control coverage, but also observation coverage — a serious missing link in many other MDV approaches. The methodology is easily integrated into existing MDV flows or can be used stand-alone.
To read the full article, click here
Related Semiconductor IP
- Peripheral Sensor Interface (PSI5) Host Controller
- Link Acceleration Unit
- 64-bit, RISC-V, ultra-high performance processors
- 64-bit, RISC-V, performance and data computation processors
- 32-bit, RISC-V, deeply embedded processors
Related Articles
- Metric Driven Verification of Reconfigurable Memory Controller IPs Using UVM Methodology for Improved Verification Effectiveness and Reusability
- How formal verification saves time in digital IP design
- Don't over-constrain in formal property verification (FPV) flows
- Formal Verification Has It Covered!
Latest Articles
- Design and Development of a Neuromorphic Silicon Suite: PVT Sensing, Stochastic LIF Inference, On-Chip STDP Learning, and Crossbar Programming
- LLM4RTL: Tool-Assisted LLM for RTL Generation
- Towards Delta Aware Training: Efficient DNN Weight Storage for Resource-Constrained FPGAs
- CHERI-D: Secure and efficient inline object ID for CHERI temporal memory safety
- AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing