5 Steps to Confront the Talent Shortage With IP-Centric Design
By Vishal Moondhra, Perforce Software
EETimes (January 4, 2024)
The talent shortage is one of the biggest challenges the U.S. semiconductor industry must confront.
According to the Semiconductor Industry Association, of the 115,000 open jobs in the industry through 2030, 58% will not be filled. The demand for these skilled employees isn’t going away anytime soon, especially as the chip industry accelerates design and production sparked by the 2022 CHIPS and Science Act. Projects are coming to market faster, budgets are tighter and teams are spread across the globe, making efficiency paramount across the board. However, U.S. chipmakers could come to a standstill if they don’t figure out how to close the talent gap.
One way to help alleviate the effects of the talent shortage is changing how semiconductors are designed so that organizations can achieve more with their existing workforce. This requires moving away from project-centric design and transitioning to an IP-centric design methodology. But why make this switch?
To read the full article, click here
Related Semiconductor IP
- Process/Voltage/Temperature Sensor with Self-calibration (Supply voltage 1.2V) - TSMC 3nm N3P
- USB 20Gbps Device Controller
- SM4 Cipher Engine
- Ultra-High-Speed Time-Interleaved 7-bit 64GSPS ADC on 3nm
- Fault Tolerant DDR2/DDR3/DDR4 Memory controller
Related White Papers
- EDA in the Cloud Will be Key to Rapid Innovative SoC Design
- FPGAs - The Logical Solution to the Microcontroller Shortage
- Paving the way for the next generation of audio codec for True Wireless Stereo (TWS) applications - PART 5 : Cutting time to market in a safe and timely manner
- It's Just a Jump to the Left, Right? Shift Left in IC Design Enablement
Latest White Papers
- Fault Injection in On-Chip Interconnects: A Comparative Study of Wishbone, AXI-Lite, and AXI
- eFPGA – Hidden Engine of Tomorrow’s High-Frequency Trading Systems
- aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on RawAudio
- Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
- Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems