Is Mignon on the edge of something big in A.I.?
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.
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– Martin
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.
“Neural networks, the dominant algorithm in A.I. and machine learning today, typically require running many layers of increasingly resource-intensive calculations. They can take a very long time and a huge amount of energy to train and deploy, and they also exist as a black box; you cannot explain why the algorithms have come to a particular conclusion,” Parkhouse-Parker says.
“Mignon is based on an algorithm that can be done in a single layer, using propositional logic, maintaining accuracy but enabling calculations to be run much more quickly, using far less energy.”
And when it comes to launching into the market, Mignon could have a strong advantage, too.
“The investment and commercial scale required for success in the semiconductor industry is significant. Some of the biggest challenges for many other competitors in this sector is that they rely on non-standard, or ‘exotic’, features which are not easily scalable within the current semiconductor manufacturing ecosystem,” says Parkhouse-Parker.
Instead, Mignon’s chipset uses a standard CMOS fabrication approach, meaning mass-production is much more straightforward.
How can it be used?
Edge A.I. has already made a notable difference to consumers’ lives. Just look at how the likes of Apple and Google have put A.I. chips into their smartphones to run tasks like face and object recognition in photos or audio transcription locally, increasing privacy and speed, and reducing data transfer costs.
Parkhouse-Parker says Mignon could eventually make a difference here, along with in the next generation of ‘6G’ telecoms networks, where signal processing could be optimised by A.I.
But the first market they’re looking at is industrial spaces where connectivity and energy resources are low, but there’s a need for high-performance A.I. classification.
And while the tech isn’t ready for it yet, Parkhouse-Parker says Mignon is working towards another selling point that its offering enables - “explainable A.I.” That is, transparency around how and why A.I. made a particular decision.
To give a timely example, if you ask OpenAI’s ChatGPT to explain a concept to you, you can’t see why it comes up with the specific answer it gives. You just get answer based on the pathway it took through its sea of data in response to your prompt.
In an industrial setting, where A.I. might be making business-critical decisions, or decisions with safety implications, it would be very useful to be able to look back and see how the A.I. came to the conclusion that it did.
“With neural networks, all of the inferences are done within a black box, and you cannot see how or why this node connects to this node, or how things have been calculated. With Mignon, because it's based on propositional logic, it allows for a researcher to be able to look in and see exactly where a decision had been made, and why, and what led it to that point,” explains Parkhouse-Parker.
Mignon wants to make it possible for this kind of accountability to be available via software, which could be appealing in fields such as medicine, defence, and the automotive industry.
Building Mignon
Mignon’s technology comes from the work of Professor Alex Yakovlev and Dr Rishad Shafik at Newcastle University.
Their research into taking the Tsetlin machine and putting it into computational hardware caught the attention of deep tech venture builder Cambridge Future Tech, which–among others–also works with GitLife Biotech and Mimicrete, who have previously featured in this newsletter.
Since spring last year, Parkhouse-Parker (Cambridge Future Tech’s COO) has been working on developing a commercial proposition for Yakovlev and Shafik’s research. He has taken the CEO role at Mignon as it spins out of the university.
Getting to market, investment, potential, and challenges
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