# Challenges of Reliability Assessment and Enhancement in Autonomous Systems

arXiv: https://arxiv.org/abs/1909.03040 · HTML: https://mlsanity.com/paper/1909.03040/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

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

- [Statistical Perspectives on Reliability of Artificial Intelligence Systems](https://mlsanity.com/paper/2111.05391/) (2021)
- [Special Session: Approximation and Fault Resiliency of DNN Accelerators](https://mlsanity.com/paper/2306.04645/) (2023)
- [Cross-Layer Optimization for Fault-Tolerant Deep Learning](https://mlsanity.com/paper/2312.13754/) (2023)

## Abstract (arXiv)

The gigantic complexity and heterogeneity of today's advanced cyber-physical systems and systems of systems is multiplied by the use of avant-garde computing architectures to employ artificial intelligence based autonomy in the system. Here, the overall system's reliability comes along with requirements for fail-safe, fail-operational modes specific to the target applications of the autonomous system and adopted HW architectures. The paper makes an overview of reliability challenges for intelligence implementation in autonomous systems enabled by HW backbones such as neuromorphic architectures, approximate computing architectures, GPUs, tensor processing units (TPUs) and SoC FPGAs.