BSIMM14 Report: Application Security Automation Soars
If you were to open an aircraft from 30 years ago, the stark contrast in technological capabilities when compared to a modern aircraft would be readily apparent to even those unfamiliar with the aerospace industry.
If it seems like everyone’s talking about multi-die systems, you’re not mistaken. The semiconductor industry isn’t only talking about them—multi-die systems are already in the market.
We’re already experiencing the effects of our world’s changing climate—devastating wildfires, prolonged droughts, torrential flooding, just to name a few examples.
Advanced robotics that can manufacture autonomous vehicles. Humanitarian mapping that addresses the impacts of environmental injustice, human rights violations, and global pandemics. Digital imagery for diabetic retinopathy screening.
2022 was a big year for the electronics industry. From the continued growth of AI (both in end devices and in chip design itself) to the emergence of more ways to design in the cloud, the level of innovation we saw was impressive.
Super-fast data transfer and efficient data processing form the backbone enabling a wide array of modern applications, from video conferencing and e-commerce to big data-fueled scientific and medical research, cryptocurrency mining, and cloud-based business collaboration.
Back in the day, it wasn’t uncommon for large semiconductor companies to maintain their own proprietary processors that were designed with their specific applications in mind.
One of the biggest advantages that the cloud provides for doing chip design using electronic design automation (EDA) tools, is the virtually unlimited and advanced compute resources that deliver the capacity chip designers need.
Catering to the requirements of a wide variety of applications while meeting time-to-market and cost targets is forcing designers to rethink memory design and verification.
With CMOS scaling at advanced nodes, there’s now greater complexity than ever. This complexity, in turn, threatens project schedules and development costs.
There’s currently no industry-standard neural network when it comes to AI hardware benchmarking, but the MLPerf benchmark suite comes close.
Investing in science, technology, engineering, and math (STEM) education is one way to nurture the interests and skillsets that are needed to bring more engineers into the workforce.