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Is Mignon on the edge of something big in A.I.?

This intriguing new approach ditches conventions... but not TOO many

Hello there,

The world of A.I.-focused chipsets is a busy place right now.

But in their efforts to outperform the competition, what if some innovators are thinking so far outside the box that a realistic go-to-market strategy can’t be anything more than a distant dream?

Today’s startup thinks it’s hit upon an approach that makes its chipset an easy sell, while offering some interesting benefits, like a potentially straightforward audit trail for why the A.I. made particular decisions.

There’s a lot going on here, so scroll down to read all about Mignon.

As usual, paying PreSeed Now members get the full story. Upgrade today if you want the full picture about this brand new university spinout.

– Martin

Editor’s note: in 2024, Mignon rebranded as Literal Labs

Mignon has a fresh proposition for A.I. on the edge

The reignited excitement around the potential of A.I. as we hurtle into 2023 brings with it concerns about how best to process all the data needed to make it work. This is far from a new challenge though, and next-generation A.I. chips are being developed in labs around the world to address the challenge in different ways.

One of the first startups we ever covered at PreSeed Now takes a ‘neuromorphic’ approach, influenced by the human brain. Coming from a different direction is a brand new spinout from Newcastle University called Mignon (so new, in fact, that there’s no website yet).

Mignon has developed an artificial intelligence chipset that, according to CEO Xavier Parkhouse-Parker, has “in the order of 10,000x performance improvements against alternative neural-network based chips for classification tasks”

Classification is, essentially, the process of figuring out what the A.I. is looking at, hearing, reading, etc - the first step in understanding the world around it, whatever use case it’s put to. Mignon’s chipset is designed to be used in edge computing as a “classification coprocessor” on devices, rather than in the cloud.

What’s more, Parkhouse-Parker says Mignon’s chipset can also train A.I. models on the edge, meaning the models can be optimised for the specific, individual environments in which they’re used. 

A propositional proposition

What Mignon says gives its tech an advantage over the competition is a less resource-intensive approach based on propositional logic.

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