Impact on the Accuracy of Aggressive Voltage Underscaling in CNN Accelerators - Toca Díaz, Yamilka (Jornada de Jóvenes Investigadores del I3A, 2023)
Chip designers usually rely on conservative supply voltage (Vdd) guardbands to prevent permanentfaults as a consequence of CMOS process variations. On the other hand, aggressively undervoltingbelow the safe voltage margin leads to huge energy savings since energy scales quadratically withVdd. Convolutional Neural Networks (CNNs) can be, to some extent, resilient to faults since theyusually include significant amounts of data redundancy. This paper shows that the accuracy of largeCNNs, like Alexnet and Squeezenet, is severely compromised when the Vdd of a CNN accelerator isreduced down to 0.54 V and 0.58 V, respectively.