32 citations · 78 across the 5 of their papers we have counts for
5 papers
Leveraging Highly Approximated Multipliers in DNN Inference
Georgios Zervakis, Fabio Frustaci, Ourania Spantidi +3
In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our ap…
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +1
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test. To bypass high energy consumption issues, appro…
Positive/Negative Approximate Multipliers for DNN Accelerators
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +2
Recent Deep Neural Networks (DNNs) managed to deliver superhuman accuracy levels on many AI tasks. Several applications rely more and more on DNNs to deliver sophisticated services…
Reliability-Aware Quantization for Anti-Aging NPUs
Sami Salamin, Georgios Zervakis, Ourania Spantidi +3
Transistor aging is one of the major concerns that challenges designers in advanced technologies. It profoundly degrades the reliability of circuits during its lifetime as it slows…
Control Variate Approximation for DNN Accelerators
Georgios Zervakis, Ourania Spantidi, Iraklis Anagnostopoulos +2
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Mont…