AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
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Abstract (arXiv)
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 5 Mar 2024.
Builds on
9 of 20 references.
References · 9
- 2022Hardware Approximate Techniques for Deep Neural Network Accelerators: A Surveycited by 39
- 2023A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networkscited by 23
- 2022AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorchcited by 11
- 2023DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Acceleratorscited by 11
- 2024Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Acceleratorscited by 9
- 2023APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errorscited by 5
- 2023Special Session: Approximation and Fault Resiliency of DNN Acceleratorscited by 5
- 2023scaleTRIM: Scalable TRuncation-Based Integer Approximate Multiplier with Linearization and Compensationcited by 3
- 2023A Novel Fault-Tolerant Logic Style with Self-Checking Capabilitycited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.