Tenstorrent Takes AI by Storm

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New 120-core Machine Learning Processor Shows Early Promise

eejournal.com, Apr. 07, 2020 – 

One thing that all machine learning developers agree upon: ML requires lots and lots of data. New ML processors have dozens – sometimes hundreds – of processor cores, huge caches, wide buses, and enormous appetites for bandwidth. The secret of ML is not that it's so radically different from normal computers, it's that it processes so much more data.

Like the real estate agents' mantra, the most important features are bandwidth, bandwidth, and bandwidth.

But what if... just suppose... you could reduce the amount of data, and therefore the awesome bandwidth, required to process ML data sets? Instead of making behemoth processors, what if we reduced the workload instead? What if we worked smarter, not harder?

That's the unique selling proposition of Tenstorrent, a 60-person ML startup that aims to make the smartest – as well as the fastest – ML processors you've ever seen. Plus, Tenstorrent's chips will scale from small IoT "at the edge" to massive cloud datacenter systems, all with the same architecture. It's a daunting task, but then, so is everything in the machine learning world.

Ljubisa Bajic, the CEO and founder of Tenstorrent, is an ex-AMD processor architect, as are a lot of his technical staff. He says the big problem with ML inference is the size of the models. Data sets are just too $^@# big for hardware to reasonably handle. Models like Google's BERT need about 680 MB; Microsoft's Turing NLG requires 34 GB, a 50-fold increase over several months. And that's just the start. Bajic says 1 TB models are on the horizon, with no end to the inflation in sight. It's gonna be tough for any chip, network, or memory technology to consume, process, and transport that much data.

So, what's the alternative? Eliminate the white space.

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