Voltage overscaling, or undervolting, is an enticing approximate technique in the context of energy-efficient Deep Neural Network (DNN) acceleration, given the quadratic relationship between power and voltage. Nevertheless, its very high error rate has thwarted its general adoption. Moreover, recent undervolting accelerators rely on 8-bit arithmetic and cannot compete with state-of-the-art low-precision (<8b) architectures. To overcome these issues, we propose a new technique called Guarded Aggressive underVolting (GAV), which combines the ideas of undervolting and bit-serial computation to create a flexible approximation method based on aggressively lowering the supply voltage on a select number of least significant bit combinations. Based on this idea, we implement GAVINA (GAV mIxed-precisioN Accelerator), a novel architecture that supports arbitrary mixed precision and flexible undervolting, with an energy efficiency of up to 89 TOP/sW in its most aggressive configuration. By developing an error model of GAVINA, we show that GAV can achieve an energy efficiency boost of 20% via undervolting, with negligible accuracy degradation on ResNet-18.

翻译：电压过缩放（或称降压）因其功率与电压间的二次方关系，在能效优化的深度神经网络（DNN）加速领域中是一种极具吸引力的近似计算技术。然而，其极高的错误率阻碍了该技术的广泛应用。此外，现有的降压加速器多依赖8位算术运算，难以与当前先进的低精度（<8位）架构竞争。为突破这些限制，我们提出一种名为“防护型激进降压”（Guarded Aggressive underVolting, GAV）的新技术，该技术融合了降压策略与位串行计算思想，通过针对选定最低有效位组合实施激进降压，形成一种灵活的近似计算方法。基于此理念，我们实现了GAVINA（GAV mIxed-precisioN Accelerator）——一种支持任意混合精度与灵活降压的新型架构，在最激进配置下能效可达89 TOP/sW。通过建立GAVINA的误差模型，我们证明GAV技术可通过降压实现20%的能效提升，且在ResNet-18网络上的精度损失可忽略不计。