# AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators

arXiv: https://arxiv.org/abs/2403.02936 · HTML: https://mlsanity.com/paper/2403.02936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks](https://mlsanity.com/paper/2305.05750/) (2023)
- [AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch](https://mlsanity.com/paper/2203.04071/) (2022)
- [DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators](https://mlsanity.com/paper/2303.08226/) (2023)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors](https://mlsanity.com/paper/2305.19733/) (2023)
- [Special Session: Approximation and Fault Resiliency of DNN Accelerators](https://mlsanity.com/paper/2306.04645/) (2023)
- [scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensation](https://mlsanity.com/paper/2303.02495/) (2023)
- [A Novel Fault-Tolerant Logic Style with Self-Checking Capability](https://mlsanity.com/paper/2306.00844/) (2023)

## Built on by (most cited first, 2 of 2)

- [PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators](https://mlsanity.com/paper/2603.08724/) (2026)
- [REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration](https://mlsanity.com/paper/2609.17555/) (2026)

## Abstract (arXiv)

In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.