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2022 technology trend review, part two: AI and graphs Posted on : Dec 22 - 2021

In the spirit of the last couple of years, we review developments in what we have identified as the key technology drivers for the 2020s in the world of databases, data management and AI. We are looking back at 2021, trying to identify patterns that will shape 2022.

Today we pick up from where we started with part one of our review, to cover AI and knowledge graphs.

The many faces of AI: Hardware, the edge, MLOps, language models, future architectures and ethics

In principle, we try to approach AI holistically. To take into account positives and negatives, from the shiny to the mundane, and from hardware to software. Hardware has been an ongoing story within the broader story of AI for the last few years, and we feel it's a good place to start our tour.

For the last couple of years, we have been keeping an eye on the growing list of "AI chips" vendors, i.e. companies that have set out to develop new hardware architectures from the ground up, aimed specifically at AI workloads. All of them are looking to get a piece of a seemingly ever-growing pie: as AI keeps expanding, said workloads keep growing, and servicing them as fast and as economically as possible is an obvious goal.

Nvidia continues to dominate this market. Nvidia was already in the market long before AI workloads started booming and had the acumen and the reflexes to capitalize on this by building a hardware and software ecosystem. Its 2020 move to make Arm a part of this ecosystem is under regulatory scrutiny. However, Nvidia did not remain idle in 2021.

Out of a slew of announcements made at Nvidia's GTC event in November 2021. the ones that bring something new on the hardware level have to do with what we would argue characterizes AI's focus in 2021 at large: inference and the edge. Nvidia introduced a number of improvements for the Triton Inference Server. It also introduced the Nvidia A2 Tensor Core GPU, a low-power, a small-footprint accelerator for AI inference at the edge that Nvidia claims offer up to 20X more inference performance than CPUs.

And what about the upstarts? SambaNova claims to now be "the world's best-funded AI startup" after a whopping $676M in Series D funding, surpassing $5B in valuation. SambaNova's philosophy is to offer "AI as a service", now including GPT language models, and it looks like 2021 was by and large a go-to-market year for them.

Xilinx, on its part, claims to achieve dramatic speed-up of neural nets versus Nvidia GPUs. Cerebras claims to 'absolutely dominate' high-end compute and scored some hefty funding too. Graphcore is competing with Nvidia (and Google) in MLPerf results. Tenstorrent hired legendary chip designer Keller. Blaize raised $71m to bring edge AI to industrial applications. Flex Logix scored $55 million in venture backing, bringing its total haul to $82 million. Last but not least, we have a new horse in the race in NeuReality, ways to mix and match deployment in ONNX and TVM, and the promise of using AI to design AI chips. If that's not booming innovation, we don't know what is. View more