Embedded Computing Design

Over the years, FPGAs have made their way into a wide variety of applications – starting from low-volume applications and migrating their way into high-growth areas such as cloud data centers and communications systems. Programmability is key in many situations where algorithms, standards, and customer needs are rapidly changing. However, this advantage has also come at a cost, because FPGAs are power-hungry and take up valuable space. 

The AI inference market has changed dramatically in the last three or four years. Previously, edge AI didn’t even exist and most inferencing capabilities were taking place in data centers, on super computers or in government applications that were also generally large-scale computing projects.

This blog discusses how to benchmark inference accelerators to find the one that is the best for your neural network.

This article will review the various generations of eFPGA, ending with the current features available today.

With the explosive growth of AI, there has become an intense focus on new specialized inferencing engines that can deliver the performance that AI demands.

Embedded FPGA is a fairly new technology, but it is quickly finding a home in a range of applications. One use growing in popularity? Connecting it to a processor bus as a reconfigurable accelerator.