Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Quantization can improve the execution latency and energy efficiency of neural networks on
both commodity GPUs and specialized accelerators. The majority of existing literature …

Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …

Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to
perform poorly at very low precision as their shrunken dynamic ranges cannot adequately …

FP8 formats are gaining popularity to boost the computational efficiency for training and
inference of large deep learning models. Their main challenge is that a careful choice of …

Low-precision data representation is important to reduce storage size and memory access
for convolutional neural networks (CNNs). Yet, existing methods have two major …

Low-precision representation of deep neural networks (DNNs) is critical for efficient
deployment of deep learning application on embedded platforms, however, converting the …

In recent years, low-precision fixed-point computation has become a widely used technique
for neural network inference on FPGAs. However, this approach has some limitations, as …

Deep neural network (DNN) models are being expanded to a broader range of applications.
The computational capability of traditional hardware platforms cannot accommodate the …

We present a fast and efficient approach for joint person detection and pose estimation
optimized for automated driving (AD) in urban scenarios. We use a multitask weight sharing …