Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Recent advancements in hardware and software systems have been driven by the
deployment of emerging smart health and mobility applications. These developments have …

Quantization is a widely used technique to compress and accelerate deep neural networks.
However, conventional quantization methods use the same bit-width for all (or most of) the …

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on
embedded devices, from smartphones to ultra low-power sensors. Due to the high …

With the development of deep neural network (DNN) techniques, applications of DNNs show
state-of-art performance. In the cloud edge collaborative mode, edge devices upload the raw …

Given a pre-trained BERT, how can we compress it to a fast and lightweight one while
maintaining its accuracy? Pre-training language model, such as BERT, is effective for …

Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput
needed for hardware deployment, particularly when the resources are limited, without …

Quantization is an effective way for reducing the memory and computation costs of deep
neural networks. Most of existing methods exploit the fixed-precision quantization approach …

As the complexity and computational demands of deep learning models rise, the need for
effective optimization methods for neural network designs becomes paramount. This work …

Visual Place Recognition (VPR) is a critical task for performing global re-localization in
visual perception systems, requiring the ability to recognize a previously visited location …