20 citations · 31 across the 9 of their papers we have counts for
9 papers
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing
Ayesha Siddique, Khurram Khalil, Khaza Anuarul Hoque
Explainable artificial intelligence (XAI) enhances AI system transparency by framing interpretability as an optimization problem. However, this approach often necessitates numerous…
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays
Ayesha Siddique, Khurram Khalil, Khaza Anuarul Hoque
Approximate deep neural networks (AxDNNs) are promising for enhancing energy efficiency in real-world devices. One of the key contributors behind this enhanced energy efficiency in…
Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults
Ayesha Siddique, Khaza Anuarul Hoque
Approximate computing is known for enhancing deep neural network accelerators' energy efficiency by introducing inexactness with a tolerable accuracy loss. However, small accuracy…
Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization
Ayesha Siddique, Khaza Anuarul Hoque
Spiking neural networks have made breakthroughs in computer vision by lending themselves to neuromorphic hardware. However, the neuromorphic hardware lacks parallelism and hence, l…
Security-Aware Approximate Spiking Neural Networks
Syed Tihaam Ahmad, Ayesha Siddique, Khaza Anuarul Hoque
Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) are both known for their susceptibility to adversarial attacks. Therefore, researchers in the recent past have extens…
RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks
Ayesha Siddique, Ripan Kumar Kundu, Gautam Raj Mode +1
The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall pr…
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
Ayesha Siddique, Khaza Anuarul Hoque
Approximate computing is known for its effectiveness in improvising the energy efficiency of deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recent…
Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators
Ayesha Siddique, Kanad Basu, Khaza Anuarul Hoque
Systolic array-based deep neural network (DNN) accelerators have recently gained prominence for their low computational cost. However, their high energy consumption poses a bottlen…
ApproxCS: Near-Sensor Approximate Compressed Sensing for IoT-Healthcare Systems
Ayesha Siddique, Osman Hasan, Faiq Khalid +1
Internet of Things (IoTs) is an emerging trend that has enabled an upgrade in the design of wearable healthcare monitoring systems through the (integrated) edge, fog, and cloud com…