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

arXiv: https://arxiv.org/abs/2101.12351 · HTML: https://mlsanity.com/paper/2101.12351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2021.

## Built on by (most cited first, 3 of 3)

- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks](https://mlsanity.com/paper/2208.00328/) (2022)

## Abstract (arXiv)

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.