Many AI-centric solutions require the utilization of Graphics Processing Units (GPUs), specifically tailored for such computational tasks. NVIDIA's solutions, recognized globally as the industry standard for such accelerators, are widely adopted. However, the high cost and limited availability of such solutions sometimes necessitate the exploration of viable alternatives.
Tensor Processing Units (TPUs), which are purpose-built for computer vision and machine learning tasks provide an alternative. These units can offer a cost-effective solution, delivering comparable computational prowess at a significantly reduced price point.
However, a notable hurdle is that most contemporary libraries and algorithms, integral to constructing AI-centric systems, are primarily adapted for GPU-based architecture and incompatible with TPUs. Recognizing this gap, our company has dedicated resources to adapt well-known algorithms and libraries to the TPU architecture, thereby facilitating the deployment of affordable and efficient intelligent systems.