Optimizing Electronics Design With AI Co-Pilots
By Ben Gu, Cadence
EETimes (November 27, 2023)
Design processes are evolving rapidly, and their use will enable the highly optimized ICs, PCBs and systems that we need to keep global innovation on track. Today’s efforts to apply analysis much earlier in the design exploration and validation process are already enabling complex multiphysics analyses and co-optimization across domains. However, increasing design complexity means we may soon need to move beyond such in-design analysis—to processes enabled by machine learning (ML) and AI.
This may sound like a reach, but ML techniques are clearly very powerful, if applied intelligently, and the one thing that the electronics industry is never short of is design data. Surely there must be a thoughtful way to bring them together.
To read the full article, click here
Related Semiconductor IP
- USB 4.0 V2 PHY - 4TX/2RX, TSMC N3P , North/South Poly Orientation
- FH-OFDM Modem
- NFC wireless interface supporting ISO14443 A and B with EEPROM on SMIC 180nm
- PQC CRYSTALS core for accelerating NIST FIPS 202 FIPS 203 and FIPS 204
- USB Full Speed Transceiver
Related White Papers
- Revolutionizing Consumer Electronics with the power of AI Integration
- Scaling AI Chip Design With NoC Soft Tiling
- Revolutionizing Chip Design with AI-Driven EDA
- Optimizing LPDDR4 Performance and Power with Multi-Channel Architectures
Latest White Papers
- FastPath: A Hybrid Approach for Efficient Hardware Security Verification
- Automotive IP-Cores: Evolution and Future Perspectives
- TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs
- How a Standardized Approach Can Accelerate Development of Safety and Security in Automotive Imaging Systems
- SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models