Thoughts on Jem Davies leading ARM's machine learning group

November 07, 2017 //By Peter Clarke
Jem Davies, the engineer who lead ARM's move into graphics processing units (GPUs), has been appointed to the position of general manager of the machine learning group at ARM. That sends a message that ARM is – at last – taking machine learning seriously.

Machine learning is a new group at ARM and is not yet even acknowledged anywhere obvious on the company’s website. However, it would seem that Davies' appointment is a tried and tested strategic move to get ARM into a business that others are pioneering and which it now wants to share in, and ultimately lead. This is what Davies did for ARM in GPUs. Prior to his leadership on GPUs ARM had been co-marketing Imagination GPUs alongside its own processor CPUs.

Over a considerable time ARM went from being an additional player in GPUs to a market leader with the combination of its Cortex processor and Mali graphics cores and the result has ultimately been the collapse of Imagination and its proposed sale to Chinese venture capital interests (see Imagination, MIPS to be sold to China-, California-connected VCs).

ARM's move to beef up its approach to machine learning is overdue but also characteristic of the UK company's conservative approach to technologies and markets. There has been criticism from observers that ARM has been slow to get on the machine learning bandwagon. For a couple of years startups with nothing to lose and established IP providers such as Cadence/Tensilica, Synopsys and Ceva have been introducing machine learning cores or have adapted DSPs and custom processors into vision processors and machine learning accelerators.

Meanwhile ARM hung back in 2016 (see ARM has R&D interest in neural network cores). And despite its characteristically thorough research position, the company made only tentative steps to support machine learning in software (see ARM's soft launch for machine learning library) and on its GPUs (see ARM's Bifrost steps up graphics, bridges to machine learning).

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