DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures

TL;DR

DNN-Life is a framework that reduces aging in on-chip weight memories of DNN hardware by combining hardware and software strategies, notably quantization analysis and duty-cycle balancing, to extend device lifetime with minimal energy cost.

Contribution

It introduces the first study of NBTI aging in DNN weight memories and proposes a novel combined hardware-software mitigation approach for aging.

Findings

Effective aging mitigation with minimal energy overhead.

Analysis of quantization impacts on memory aging.

Enhanced lifetime of DNN hardware components.

Abstract

Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability threats in nano-scale devices. This paper makes the first attempt to study the NBTI aging in the on-chip weight memories of deep neural network (DNN) hardware accelerators, subjected to complex DNN workloads. We propose DNN-Life, a specialized aging analysis and mitigation framework for DNNs, which jointly exploits hardware- and software-level knowledge to improve the lifetime of a DNN weight memory with reduced energy overhead. At the software-level, we analyze the effects of different DNN quantization methods on the distribution of the bits of weight values. Based on the insights gained from this analysis, we propose a micro-architecture that employs low-cost memory-write (and read) transducers to achieve an optimal duty-cycle at run time in the weight memory cells, thereby balancing their aging. As a result, our DNN-Life framework enables efficient aging mitigation of weight memory of the given DNN hardware at minimal energy overhead during the inference process.