A six-step multiphysics simulation uses electrical and thermal parameters to design and validate any battery model.
Battery powered electric vehicles (EVs) and hybrid electric vehicles (HEVs) have been gaining market share as the public’s environmental concerns and the depletion of fossil fuels drive the demand for better fuel efficiency. But great improvements in the mileage one can drive before recharging, a faster recharging method, and the creation of a recharging infrastructure akin to today’s gas stations are needed before fully electric vehicles will be practical for day-to-day use.
An increase in battery efficiency is one of the biggest challenges engineers face. While Li-ion batteries are currently the best option available, they are by no means the ultimate in battery technology: there is much room for improvement. The high mass of Li-ion batteries reduces fuel efficiency in automobiles, and their potential flammability makes them dangerous, particularly in the event of a crash. Researchers worldwide are trying to develop a better battery to replace Li-ion technology. For now, optimizing the configuration of Li-ion batteries is the best method for increasing battery performance.
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New interconnects and processes will be required to reach the next process nodes.
Servers today feature one or two x86 chips, or maybe an Arm processor. In 5 or 10 years they will feature many more.
After failing in the fab race, the country has started focusing on less capital-intensive segments.
Rowhammer attack on memory could create significant issues for systems; possible solution emerges.
The backbone of computing architecture for 75 years is being supplanted by more efficient, less general compute architectures.
Servers today feature one or two x86 chips, or maybe an Arm processor. In 5 or 10 years they will feature many more.
Chips are hitting technical and economic obstacles, but that is barely slowing the rate of advancement in design size and complexity.
Predicting the power or energy required to run an AI/ML algorithm is a complex task that requires accurate power models, none of which exist today.
Tradeoffs in AI/ML designs can affect everything from aging to reliability, but not always in predictable ways.
As implementations evolve to stay relevant, a new technology threatens to overtake SerDes.
What’s required to optimize your design for energy? The simple answer is a new EDA flow that goes from conception to implementation.
New technology could have an impact on NVM, in-memory processing, and neuromorphic computing.
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