Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

This article presents a survey of the different modern high-level synthesis (HLS) design
space exploration (DSE) techniques that have been proposed so far to automatically …

The exploding complexity and computation efficiency requirements of applications are
stimulating a strong demand for hardware acceleration with heterogeneous platforms such …

Agile hardware development requires fast and accurate circuit quality evaluation from early
design stages. Existing work of high-level synthesis (HLS) performance prediction usually …

State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be
accommodated on resource constrained embedded devices. Moreover, with the …

Hardware accelerators based on field programmable gate array (FPGA) and system on chip
(SoC) devices have gained attention in recent years. One of the main reasons is that these …

High-level synthesis (HLS) tools have gained great attention in recent years because it
emancipates engineers from the complicated and heavy hardware description language …

Despite the great advance achieved by electronic design automation (EDA) tools, there is
still a long way towards hardware agile development, whose ultimate goal is to reduce chip …

Transformer neural networks (TNNs) are being applied across a widening range of
application domains, including natural language processing (NLP), machine translation, and …

Fast and accurate depth estimation, or stereo matching, is essential in embedded stereo
vision systems, requiring substantial design effort to achieve an appropriate balance among …