Given the stringent requirements of energy efficiency for Internet-of-Things edge devices,
approximate multipliers, as a basic component of many processors and accelerators, have …

Abstract “How much energy is consumed for an inference made by a convolutional neural
network (CNN)?” With the increased popularity of CNNs deployed on the wide-spectrum of …

Approximate Computing (AxC) has emerged as a promising paradigm to enhance
performance and energy efficiency by allowing a controlled trade-off between accuracy and …

We present the concept of approximate intermittent computing and concretely demonstrate
its application. Intermittent computations stem from the erratic energy patterns caused by …

Deep neural network (DNN) inference demands substantial computing power, resulting in
significant energy consumption. A large number of negative output activations in convolution …

Dense SLAM is an important application on an embedded environment. However,
embedded platforms usually fail to provide enough computation resources for high-accuracy …

Reducing energy consumption while providing performance and quality guarantees is
crucial for computing systems ranging from battery-powered embedded systems to data …

Approximate arithmetic circuits (AACs) are increasingly investigated to design high-
performance and energy-efficient hardware for error-tolerant applications. However, it is of …

This dissertation investigates design techniques involving custom Floating-Point (FP)
computation for low-power neural network accelerators in resource-constrained embedded …

Abstract Machine learning has proliferated on many Internet-of-Things (IoT) applications
designed for edge devices. Energy efficiency is one of the most crucial constraints in the …