Maximize performance in the thermal envelope.
The modern System-on-Chip (SoC) has higher thermal dissipation than its previous generations, because of the following factors:
Faster frequencies mean faster switching, which means more power consumption and more heat dissipation. Smaller size means smaller thermal mass, which makes it more difficult to transfer heat quickly. As a result, power density has increased rapidly, and SoC temperature has now become a fundamental design bottleneck. Therefore, you need a thermal management solution to keep an SoC within a defined temperature range.
Most ARM-based systems do not have fans. Therefore, you must take special care to keep system temperatures within acceptable bounds.
To address this challenge, ARM has developed Intelligent Power Allocation (IPA) to keep an SoC within its thermal envelope by dynamically tuning the power allocation according to the current temperature.
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Interest in the open-source ISA marks a significant shift among chipmakers, but it will require continued industry support to be successful.
Slowdown due to impact on timing, and dependencies between power, thermal and timing that may not be caught by signoff tools.
Access to source code makes it attractive for custom applications, but gaps remain in the tool flow and in software.
SoC bandwidth, integration expand as data centers use more FPGAs for machine learning.
Problems won’t derail next-gen litho, but could limit use and affect schedules.
Using the Von Neumann architecture for artificial intelligence applications is inefficient. What will replace it?
Interest in the open-source ISA marks a significant shift among chipmakers, but it will require continued industry support to be successful.
Assessing the reliability of a device requires adding more physical factors into the analysis, many of which are interconnected in complex ways.
Slowdown due to impact on timing, and dependencies between power, thermal and timing that may not be caught by signoff tools.
Improvements in power, performance and area are much more difficult to achieve, but solutions are coming into focus.
Shift to cloud model has kept power consumption in check, but that benefit may have run its course.
Old solutions don’t necessarily work anymore, particularly at advanced nodes and ultra-low voltage.
Edge compute covers a wide range of applications. Understanding bandwidth and capacity needs is critical.
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