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cs.CR2021★ 1 cited
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…
cs.LG2021
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…