Fundamentals of High-Level Synthesis Part 1: Basic Concepts

With the emergence of compute-intensive applications in different areas such as Artificial Intelligence (AI), Machine Learning (ML), Convolutional Neural Networks (CNN), Internet of Things (IoT), image processing, computer vision, advanced driver-assistance systems (ADS) just to name a few, the demand for application-specific accelerators has risen. The main goal of these accelerators is improving the performance by reducing the latency and increasing the throughput. However, reducing energy consumption is also a key factor for using the accelerators in today embedded systems which commonly draw their power from batteries. To address this demand, designers have been exploring three computational platforms, including FPGA, GPU, and ASIC.

GPUs are the hardware platforms that have been adapted for accelerating compute-intensive parallel algorithms which cover most of the new applications. The availability, easy programming, and debugging are the main benefits of these platforms. However, high energy consumption is the main drawback that prevents them from using in energy-critical embedded systems. Therefore, researchers are working to make them energy-efficient in such systems. A sound track record of research and development products is Nvidia Jetson Family.

ASICs have recently been the main focus of the industry to provide AI-related hardware accelerators. Examples are Intel® Movidius™ Neural Compute Stick and Google Coral USB. Although these accelerators have been successful in…