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Found 6 papers, 0 papers with code
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.
Special Session: Approximation and Fault Resiliency of DNN Accelerators
We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN.
APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns.
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
Through this SLR, three kinds of methods for reliability assessment of DNNs are identified including Fault Injection (FI), Analytical, and Hybrid methods.
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
The framework enables selective approximation of reliability-critical DNNs, providing a set of Pareto-optimal DNN implementation design space points for the target resource utilization requirements.
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment
In this work, we propose a novel accurate, fine-grain, metric-oriented, and accelerator-agnostic method called DeepVigor that provides vulnerability value ranges for DNN neurons' outputs.
