  Transistor aging is one of the major concerns that challenges designers in
advanced technologies. It profoundly degrades the reliability of circuits
during its lifetime as it slows down transistors resulting in errors due to
timing violations unless large guardbands are included, which leads to
considerable performance losses. When it comes to Neural Processing Units
(NPUs), where increasing the inference speed is the primary goal, such
performance losses cannot be tolerated. In this work, we are the first to
propose a reliability-aware quantization to eliminate aging effects in NPUs
while completely removing guardbands. Our technique delivers a graceful
inference accuracy degradation over time while compensating for the
aging-induced delay increase of the NPU. Our evaluation, over ten
state-of-the-art neural network architectures trained on the ImageNet dataset,
demonstrates that for an entire lifetime of 10 years, the average accuracy loss
is merely 3%. In the meantime, our technique achieves 23% higher performance
due to the elimination of the aging guardband.
