A Brief Guide to NVIDIA Tensor Technology for Edge AI
Edge computing has quickly become the computing environment of choice for many
industrial processes. However, this system has developed remarkably with the introduction
and continued developments of artificial intelligence. Edge AI sees the edge of a network
benefiting from AI computations, which can help with the efficiency, accuracy, and
effectiveness of processes.
Yet as mentioned, edge AI continues to experience new developments that help to refine its
productiveness. This is evidenced by NVIDIA Tensor technology. Known as Tensor Cores, this
technology is helping to take AI to new heights.
What is NVIDIA Tensor Core Technology?
2017 was the year that NVIDIA first introduced its Tensor
Core technology. Ever since then, system engineering
specialists like things-embedded.com have leveraged this
technology to improve their offerings. Computer systems
have become more powerful and refined as a result – and
it has only continued in an upward trajectory. Now into its
fourth generation with the H100, NVIDIA’s Tensor Cores
have boosted performance by 60x since its inception.
As for what this Tensor Core technology entails, it opens
the door to mixed-precision computing. This is achieved
by adapting calculations dynamically, allowing accuracy to
be preserved while accelerating throughput. Its impressive
capabilities are highlighted by its 6x speedups for
transformer network training, along with a 3x
improvement in performance for every application.
The Benefits of Using NVIDIA Tensor Core Technology for Edge AI
As you can likely gather from the above information, it’s beneficial for edge AI to make use of
NVIDIA Tensor Core technology. However, how can it specifically help an organization? To
answer that question, here are some of the key benefits:
Innovative AI Training
It’s no secret that AI models are becoming increasingly complex by the minute. For AI
technology to handle this complexity, it needs the right support – and that’s what NVIDIA
Tensor Core provides. Rather than taking weeks, possibly even months to train AI models in
FP32, this time can be reduced significantly with NVIDIA Tensor Core. Yet, even though those
training-to-convergence times drop dramatically, accuracy is never lost during the process.
Unprecedented AI Inference
An AI inference accelerator needs to cover many different areas. It requires great performance
as standard. It requires the ability to ensure diverse neutral networks are accelerated. It also
has to supply enough programmability so developers are capable of building new ones on
their own. NVIDIA Tensor Cores deliver in all of these areas. This technology boasts
unprecedented performance and versatility thanks to maximizing utilization and sticking to
low latency at high throughput.
Advanced HPC
Advanced HPC capabilities are another key benefit of using NVIDIA Tensor Cores. HPC, aka
high performance computing, is a necessity of modern AI technologies. By being able to
process data at high speeds, along with completing complex calculations in no time, it
ensures edge AI systems are able to maximize their performance levels. With this, an
organization is able to solve large problems that would otherwise be a difficult and time-
consuming task.