AI has spawned a new generation of chips designed to strike a balance between throughput, latency, and power consumption.
AI accelerators like GPUs, FPGAs, and vision processing units (VPUs) are optimized to compute neural network workloads. These processor architectures are empowering applications like computer vision (CV), speech recognition, and natural language processing. They are also enabling local AI inferencing on IoT edge devices.
But benchmarks show that these accelerators are not created equal. Choosing one can have serious implications on system throughput, latency, power consumption, and overall cost.
This technology brief shows how innovation in chip architectures and hardware accelerators is enabling AI at the edge. While each architecture has its merits, it’s critical to consider how these platforms impact the compute performance, power consumption, and latency of neural network operations and systems as a whole.