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Found 12 papers, 0 papers with code
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications.
AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms, i. e. reducing the energy consumption and execution time as well as increasing the efficiency of DNN accelerators.
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.
A Novel Fault-Tolerant Logic Style with Self-Checking Capability
We introduce a novel logic style with self-checking capability to enhance hardware reliability at logic level.
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.
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.
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.
Noise-Tolerance GPU-based Age Estimation Using ResNet-50
In recent years, a person's age has been studied as one of the important features of the face.
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment
In this paper, we discuss several shortcomings of prior base callers in the case of time-critical applications, privacy-aware design, and the problem of catastrophic forgetting.
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.
