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Found 4 papers, 0 papers with code
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving.
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.
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.
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.
