Hardware Overview and Complexities Faced by EAI

Hardware Overview And Complexities Faced By EAI

Embedded AI hardware can be broadly categorized into two segments, general purpose and custom hardware, which can further be classified into the following subtypes:

Figure 3: Embedded AI hardware classification

Embedded AI hardware ranges from general-purpose CPUs and GPUs, FPGAs, and special-purpose ASICs. Hardware accelerators can reduce latency and increase throughput, thereby making AI algorithms such as convolution layers of CNNs a perfect target for FPGA-based acceleration. These AI model complexities, involving software-hardware co-design, are what drives the need for hardware accelerators that can support the training and inference needs of AI.

For industrial applications such as robots, autonomous vehicles, and drones – low latency and power efficiency constitute some of the key hardware requirements. The sensors in these units must react instantly to enable autonomy and safety — something that traditional computing architectures are not designed to provide. Addressing these requirements involves moving data between the memory and the processors, as well as introducing high latency and power. Traditional computing architecture where not designed for such applications and this makes these architectures extremely inefficient for EAI applications.