Optimize SoC Design with a Network-on-Chip Strategy
By Andy Nightingale, Arteris
Utilizing physically aware interconnect IP from trusted third-party vendors can reduce design time and increase productivity.
Today’s system-on-chip (SoC) devices can contain hundreds of millions to over a hundred billion transistors, depending on the application. The only way to create designs of this complexity is to employ large numbers of functional blocks called intellectual-property (IP) blocks or IPs.
Many of these blocks embody well-known and standard functions, such as processor cores, communication cores (Ethernet, USB, I2C, SPI, etc.) and peripheral processes. Rather than spend valuable time and resources re-implementing these functions from scratch, SoC design teams acquire these IPs from respected third-party vendors.
Access to robust, tested, and proven IP speeds up the development process and reduces risk. Using third-party IP for common functions frees the SoC design team to focus on their own “secret sauce” IP blocks, which will differentiate their SoC from competitive offerings.
To read the full article, click here
Related Semiconductor IP
- UCIe D2D Adapter & PHY Integrated IP
- Low Dropout (LDO) Regulator
- 16-Bit xSPI PSRAM PHY
- MIPI CSI-2 CSE2 Security Module
- ASIL B Compliant MIPI CSI-2 CSE2 Security Module
Related Articles
- SoC design: When is a network-on-chip (NoC) not enough?
- SoC design: When a network-on-chip meets cache coherency
- A logically correct SoC design isn’t an optimized design
- Integrating VESA DSC and MIPI DSI in a System-on-Chip (SoC): Addressing Design Challenges and Leveraging Arasan IP Portfolio
Latest Articles
- RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification
- Emulation-based System-on-Chip Security Verification: Challenges and Opportunities
- A 129FPS Full HD Real-Time Accelerator for 3D Gaussian Splatting
- SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation
- TensorPool: A 3D-Stacked 8.4TFLOPS/4.3W Many-Core Domain-Specific Processor for AI-Native Radio Access Networks