Analog behavioral models reduce mixed-signal LSI verification time
By Takao Ito, Chief Specialist, Toshiba Corporation
Jun 22, 2007 (12:52 PM) -- Planet Analog
Figure 1a: CPU performance and simulation verification trend; taller (blue) bars are CPU performance, lower (yellow) bars are verification times
Figure 1b: Verification time trend
Jun 22, 2007 (12:52 PM) -- Planet Analog
Smaller process geometries are making it possible to take analog components off the board and incorporate them into the chip together with the digital portions of the designs, increasing the complexity of circuits. Even though there is a rapid increase in today's processor performance, simulation for full-chip verification is still taking a long time (Figure 1a and Figure 1b).
Figure 1a: CPU performance and simulation verification trend; taller (blue) bars are CPU performance, lower (yellow) bars are verification times
Figure 1b: Verification time trend
Current methodologies are no longer sufficient or acceptable, so new verification methods are needed.
To read the full article, click here
Related Semiconductor IP
- DeWarp IP
- 6-bit, 12 GSPS Flash ADC - GlobalFoundries 22nm
- LunaNet AFS LDPC Encoder and Decoder IP Core
- ReRAM NVM in DB HiTek 130nm BCD
- UFS 5.0 Host Controller IP
Related Articles
- Efficient Verification and Virtual Prototyping of Analog and Mixed-Signal IP and SOCs Using Behavioral Models
- Mixed-signal SOC verification using analog behavioral models
- Reuse of Analog Mixed Signal IP for SoC Design: Progress Report (Cadence Design Systems)
- Analog & Mixed Signal IC Debug: A high precision ADC application
Latest Articles
- VolTune: A Fine-Grained Runtime Voltage Control Architecture for FPGA Systems
- A Lightweight High-Throughput Collective-Capable NoC for Large-Scale ML Accelerators
- Quantifying Uncertainty in FMEDA Safety Metrics: An Error Propagation Approach for Enhanced ASIC Verification
- SoK: From Silicon to Netlist and Beyond Two Decades of Hardware Reverse Engineering Research
- An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks