Adapting DNNs to Embrace Voltage Scaling Induced Errors via Accumulation-Aware Quantization

The pervasive deployment of Deep Neural Networks (DNNs) across the computing spectrum has brought growing attention to energy efficiency, alongside raw performance. While voltage scaling is a potent knob for power reduction, its application in next-generation nodes is hampered by severe throughput degradation and susceptibility to process variation. This work aims to address these energy-reliability trade-offs by introducing a hardware-software co-design methodology, based on the observation th…

Source: arXiv cs.AR (Hardware Architecture), published 2026-10-27. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern