AI startup Wave Computing announced this week that it has acquired MIPS Tech, Inc. (formerly MIPS Technologies), a global leader in RISC processor Intellectual Property (IP) and licensable CPU cores. The acquisition will accelerate Wave's strategy of offering AI acceleration from the Datacenter to the Edge of Cloud by extending the company's products beyond AI systems to now also include AI-enabled embedded solutions. This is a major milestone not only in the history of our two companies, but also for the AI compute industry," said Derek Meyer, CEO of Wave Computing. "With working DPU commercial silicon and being in the final stages of bringing our first AI systems to market, now is the time for us to expand to the Edge of Cloud. The acquisition of MIPS allows us to combine technologies to create products that will deliver a single'Datacenter-to-Edge' platform ideal for AI and deep learning.

Wave Computing, a Silicon Valley AI startup specializing in data flow processing of Deep Neural Networks, has acquired MIPS Technologies for an undisclosed amount. Wave projects that the acquisition will be immediately cash-flow positive and accretive to its balance sheet and valuation. The deal logic is pretty sound, adding new markets such as edge AI computing while giving the company in-house RISC cores it can use for its next-generation DataFlow Processing Unit datacenter AI chip. Who is Wave Computing, and why does it need MIPS? Wave is an early innovator in AI silicon geared towards datacenter use, to train deep neural networks (DNNs) and run those networks for predictions and classifications.

When it comes to deep learning chip startups, hype moves fast but crossing the finish line to real production silicon takes an incredibly long time. There are several incumbents on the custom hardware side aiming for the AI training and inference market but outside of Google's TPU, there are very few functioning inside datacenters. From the forthcoming Nervana chips (now expected in 2019) to startups like Graphcore, Cerebras (which just ducked back into stealth mode), among several others, the pressure is on to create hardware that reflects the latest framework and algorithmic developments that so far seem to run quite well on widely available GPUs with all the requisite porting and software work handled thanks to big library and tooling investments from Nvidia over the last few years. In other words, it is going to be damn tough to beat Nvidia, especially this late in the game, but for one of the better known deep learning chip startups, Wave Computing, there is more going on in the outfield than we might readily see. This is why the company has invested in tech that might seem a bit left field--that is, until we look at how the AI hardware game of the future might play out.