arXiv:2606.04266v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are used in a variety of real-world applications including, for example, image classification and speech recognition. The inference accuracy of DNN implemented on hardware in integrated circuits (ICs) degrades under phenomena such as transistor aging. Aging slows down the switching speed of transistors, resulting in system-level timing violations due to unsustainable clocks. To maintain reliability for the entire projected lifetime, designers add guardbands to prevent timing violations; however, adding large timing g
The increasing scale and deployment of DNNs in critical applications highlight the need for long-term reliability and performance consistency, making transistor aging a more pressing concern.
Transistor aging directly impacts the reliability and sustained performance of AI hardware, requiring proactive design and mitigation strategies beyond traditional guardbanding.
Hardware design for AI will increasingly incorporate aging-aware techniques, moving beyond simple timing guardbands to maintain DNN inference accuracy over extended operational lifetimes.
- · Semiconductor manufacturers
- · AI hardware designers
- · Reliability engineering firms
- · Companies deploying long-lifecycle AI systems
- · AI hardware solutions without aging mitigation
- · Companies reliant on short-term hardware optimization only
This research will lead to the development of more robust and reliable AI accelerators and specialized compute units.
Improved hardware reliability will enable wider adoption of AI in safety-critical and infrastructure applications with long operational lifespans.
The necessity for aging-aware design could influence future semiconductor process development, emphasizing material science and transistor longevity.
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