While some of the largest technology companies in the world are racing to figure out the next generation of machine learning-focused chips that will support devices – whether that’s data centers or edge devices – there’s a whole class of startups that are racing to get there first.
That includes Cerebras Systems, one of the startups that has raised a significant amount of capital, which is looking to continue targeting next-generation machine learning operations with the hiring of Dhiraj Mallick as its Vice President of Engineering and Business Development. Prior to joining Cerebras, Mallick served as the VP of architecture and CTO of Intel’s data center group. That group generated more than $5.5 billion in the second quarter this year, up from nearly $4.4 billion in the second quarter of 2017, and has generated more than $10 billion in revenue in the first half of this year. Prior to Intel, Mallick spent time at AMD and SeaMicro.
That latter part is going to be a big part of the puzzle, as Google looks to lock in customers in its cloud platform with tools like the Tensor Processing Unit, the third generation of which was announced at Google I/O earlier this year. Data centers are able to handle some of the heavy lifting when it comes to training the models that handle machine learning processes like image recognition as they don’t necessarily have to worry about space (or partly heat, in the case of the TPU running with liquid cooling) constraints. Google is betting on that with the TPU, optimizing its hardware for its TensorFlow machine learning framework and trying to build a whole developer ecosystem that it can lock into its hardware with that and its new edge-focused TPU for inference.
Cerebras Systems is one of a class of startups that want to figure out what the next generation of machine hardware looks like, and most of them have raised tens of millions of dollars. It’s one of the startups that has been working on its technology for a considerable amount of time. Others include Mythic, SambaNova, Graphcore, and more than a dozen others that are all looking at different pieces of the machine learning ecosystem. But the end goal for all of them is to capture part of the machine learning process – whether that’s inference on the device or training in a server somewhere – and optimize a piece of hardware for just that.
And while Google looks to lock in developers into its TensorFlow ecosystem with the TPU, that there are a number of different frameworks for machine learning may actually open the door for some startups like the ones mentioned above. There are frameworks like PyTorch and Caffe2, and having a kind of third-party piece of equipment that works across a number of different developer frameworks may end up being attractive to some companies. Nvidia has been one of the largest beneficiaries here of the emergence of GPUs as a go-to piece of hardware for machine learning, but these startups all bet on room for a new piece of hardware that’s even better at those specialized operations.