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
- RVA23, Multi-cluster, Hypervisor and Android
- 64 bit RISC-V Multicore Processor with 2048-bit VLEN and AMM
- NPU IP Core for Mobile
- RISC-V AI Acceleration Platform - Scalable, standards-aligned soft chiplet IP
- H.264 Decoder
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
- QiMeng: Fully Automated Hardware and Software Design for Processor Chip
- RISC-V source class riscv_asm_program_gen, the brain behind assembly instruction generator
- Concealable physical unclonable functions using vertical NAND flash memory
- Ramping Up Open-Source RISC-V Cores: Assessing the Energy Efficiency of Superscalar, Out-of-Order Execution
- Transition Fixes in 3nm Multi-Voltage SoC Design