Real-time visual object tracking provides every object of interest with a unique identity and a
trajectory across video frames. This is a fundamental task of many video analytics …

The intricacy of the Deep Learning (DL) landscape, brimming with a variety of models,
applications, and platforms, poses considerable challenges for the optimal design …

Deep Learning (DL) is moving towards deploying workloads not only in cloud datacenters,
but also to the local devices. Although these are mostly limited to inference tasks, it still …

Convolutional Neural Networks (CNNs) are currently widely used in various fields,
particularly for computer vision applications. Edge platforms have drawn tremendous …

With more powerful yet efficient embedded devices and accelerators being available for
Deep Neural Networks (DNN), machine learning is becoming an integral part of edge …

Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a
challenging task that requires tailored hardware accelerator architectures and a clear …

To achieve high-throughput deep learning (DL) model inference on heterogeneous
multiprocessor systems-on-chip (HMPSoC) platforms, the use of pipelining for the …

For heterogeneous multiprocessor system-on-chips (HMPSoCs), a reasonable pipeline
design can significantly improve the inference performance of deep learning (DL) models …

With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for
Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms …

Our society will be deeply impacted by neural network inference on embedded devices.
Many of them are based on the use of microcontroller units (MCUs) which are extremely …