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
- Chiplet Die-to-Die Interconnect IP Solution
- High speed MACsec Engine 100G/200G/400G/800G/1.6T
- Temperature/Voltage sensors
- AMBA Bus Host to eSPI Controller/Target
- AMBA Bus Host to eSPI Controller
Related Articles
- 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
- How to Elevate RRAM and MRAM Design Experience to the Next Level
- How to accelerate memory bandwidth by 50% with ZeroPoint technology
Latest Articles
- ZK-Flex: A Flexible and Scalable Framework for Accelerating Zero-Knowledge Proofs
- ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training
- OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs
- CHIMERA: A Flexible and Scalable 3.1 TOPS/W AI-MCU with Transformer Accelerator and 563 Gb/s Shared-L2 Memory Subsystem with QoS Guarantees
- CXL-ClusterSim: Modeling CXL-based Disaggregated Memory Cluster for Pooling and Sharing using gem5 and SST