{"id":603143,"date":"2023-01-31T07:49:00","date_gmt":"2023-01-31T13:49:00","guid":{"rendered":"https:\/\/news.sellorbuyhomefast.com\/index.php\/2023\/01\/31\/a-new-way-of-doing-artificial-intelligence-uks-mignon-has-a-fresh-proposition-for-ai-on-the-edge\/"},"modified":"2023-01-31T07:49:00","modified_gmt":"2023-01-31T13:49:00","slug":"a-new-way-of-doing-artificial-intelligence-uks-mignon-has-a-fresh-proposition-for-ai-on-the-edge","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2023\/01\/31\/a-new-way-of-doing-artificial-intelligence-uks-mignon-has-a-fresh-proposition-for-ai-on-the-edge\/","title":{"rendered":"\u2018A new way of doing artificial intelligence\u2019: UK\u2019s Mignon has a fresh proposition for AI on the edge"},"content":{"rendered":"
\n

This story is syndicated<\/a> from the premium edition of PreSeed Now<\/a>, a newsletter that digs into the product, market, and founder story of UK-founded startups so you can understand how they fit into what\u2019s happening in the wider world and startup ecosystem.<\/em><\/p>\n

The reignited excitement around the potential of AI 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 AI chips are being developed in labs around the world to address the challenge in different ways.<\/span><\/p>\n

One of the first startups we ever covered at PreSeed Now<\/span> takes a \u2018neuromorphic\u2019 approach<\/span><\/a>, influenced by the human brain. Coming from a different direction is a brand new spinout from Newcastle University called <\/span>Mignon (so new, in fact, that there\u2019s no website yet).<\/span><\/p>\n

Mignon has developed an artificial intelligence chipset that, according to CEO<\/span> Xavier Parkhouse-Parker<\/span><\/a>, has \u201cin the order of 10,000x performance improvements against alternative neural-network based chips for classification tasks\u201d<\/span><\/p>\n

Classification<\/span><\/a> is, essentially, the process of figuring out what the AI is looking at, hearing, reading, etc \u2014 the first step in understanding the world around it, whatever use case it\u2019s put to. Mignon\u2019s chipset is designed to be used in edge computing as a \u201cclassification coprocessor\u201d on devices, rather than in the cloud.<\/span><\/p>\n

What\u2019s more, Parkhouse-Parker says Mignon\u2019s chipset can also train AI models on the edge, meaning the models can be optimised for the specific, individual environments in which they\u2019re used.<\/span><\/p>\n

A prototype design of Mignon\u2019s gen-1 chipset<\/figcaption><\/figure>\n

A propositional proposition<\/h2>\n

What Mignon says gives its tech an advantage over the competition is a less resource-intensive approach based on<\/span> propositional logic<\/span><\/a>.<\/span><\/p>\n

\u201cNeural networks, the dominant algorithm in AI 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,\u201d Parkhouse-Parker says.<\/span><\/p>\n

\u201cMignon 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.\u201d<\/span><\/p>\n

And when it comes to launching into the market, Mignon could have a strong advantage, too.<\/span><\/p>\n

\u201cThe 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 \u2018exotic\u2019, features which are not easily scalable within the current semiconductor manufacturing ecosystem,\u201d says Parkhouse-Parker.<\/span><\/p>\n

Instead, Mignon\u2019s chipset uses a standard<\/span> CMOS<\/span><\/a> fabrication approach, meaning mass-production is much more straightforward.<\/span><\/p>\n

How can it be used?<\/h2>\n

Edge AI has already made a notable difference to consumers\u2019 lives. Just look at how the likes of Apple and Google have put AI 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.<\/span><\/p>\n

Parkhouse-Parker says Mignon could eventually make a difference here, along with in the next generation of \u20186G\u2019 telecoms networks, where signal processing could be optimised by AI<\/span><\/p>\n

But the first market they\u2019re looking at is industrial spaces where connectivity and energy resources are low, but there\u2019s a need for high-performance AI classification.<\/span><\/p>\n

And while the tech isn\u2019t ready for it yet, Parkhouse-Parker says Mignon is working towards another selling point that its offering enables \u2014 \u201cexplainable AI.\u201d That is, transparency around how and why AI made a particular decision.<\/span><\/p>\n

To give a timely example, if you ask OpenAI\u2019s<\/span> ChatGPT<\/span><\/a> to explain a concept to you, you can\u2019t 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.<\/span><\/p>\n

In an industrial setting, where AI 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 AI came to the conclusion that it did.<\/span><\/p>\n

\u201cWith 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\u2019s 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,\u201d explains Parkhouse-Parker.<\/span><\/p>\n

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.<\/span><\/p>\n

The brains behind the Mignon product. L-R: Professor Alex Yakovlev and Dr Rishad Shafik<\/figcaption><\/figure>\n

Building Mignon<\/b><\/h2>\n

Mignon\u2019s technology comes from the work of<\/span> Professor Alex Yakovlev<\/span><\/a> and<\/span> Dr Rishad Shafik<\/span><\/a> at Newcastle University.<\/span><\/p>\n

Their research into taking the<\/span> Tsetlin machine<\/span><\/a> and putting it into computational hardware caught the attention of deep tech venture builder<\/span> Cambridge Future Tech<\/span><\/a>, which\u2013among others\u2013also works with<\/span> GitLife Biotech<\/span><\/a> and<\/span> Mimicrete<\/span><\/a>, who have previously featured in this newsletter.\u00a0<\/span><\/p>\n

Since spring last year, Parkhouse-Parker (Cambridge Future Tech\u2019s COO) has been working on developing a commercial proposition for Yakovlev and Shafik\u2019s research. He has taken the CEO role at Mignon as it spins out of the university.<\/span><\/p>\n

Getting to market<\/h2>\n

First on the to-do list for the new startup is further refining its technology with the development of a \u2018generation-2\u2019 chipset before they bring it to market.\u00a0<\/span><\/p>\n

\u201cEven though we\u2019ve got fantastic performance improvements, and it\u2019s actually quite remarkable, this has all been done on the 65-nanometer node, which is an old technology and should mean worse performance improvements, because effectively the transistors are bigger, and that\u2019s what makes us really remarkable,\u201d says Parkhouse-Parker.\u00a0<\/span><\/p>\n

\u201cWe think that when we move to a 28-nanometer node, that all of the numbers we have the benchmarks are going to be significantly greater at this scale.\u201d<\/span><\/p>\n

Commercial validation is obviously another important step after that. The eventual goal is to partner with fabless chip companies to build the Mignon technology into a commercially available system-on-a-chip. Mignon has a number of hires planned for the near future to help it get there.<\/span><\/p>\n

Mignon CEO, Xavier Parkhouse-Parker<\/figcaption><\/figure>\n

Investment plans and future potential<\/b><\/h2>\n

Parkhouse-Parker expects the spin-out process to be complete in March this year, after which they will formally open a \u00a32.55 million funding round.<\/span><\/p>\n

This will be used to expand the team, develop, test, and fabricate the next generation of chipset, and to get commercial validation in a number of verticals. Software to allow AI development on the chipset is also a key part of the roadmap.<\/span><\/p>\n

Eventually, Parkhouse-Parker wants Mignon\u2019s combination of low-power performance and widespread compatibility to usher in whole new opportunities for AI<\/span><\/p>\n

\u201cWhat Mignon does is open up a possibility for what is genuinely a completely new world of devices that people haven\u2019t even thought about yet. Think about the opportunities that would be there with product people like a Steve Jobs or a Jony Ive that could use this and run wild with the potential. I think there really is a completely new world of possibilities.\u201d<\/span><\/p>\n

The big \u201chump\u201d<\/b><\/h2>\n

There\u2019s no clear road from where Mignon is now to that future. Aside from the additional development work to refine the chipset, there\u2019s a shift in mindset required from the people who build AI applications.<\/span><\/p>\n

\u201cThe big \u2018hump\u2019, as one of our advisors calls it, is that it\u2019s a new way of doing artificial intelligence,\u201d says Parkhouse-Parker. \u201cThe transition between neural networks and Tsetlin is not incredibly significant, but it will require a little bit of a mindset difference. It may require new ways of thinking around how artificial intelligence problems can be designed and how these things can be brought into market.<\/span><\/p>\n

\u201cThere\u2019s a great community already being built around this, but that\u2019s one of the biggest challenges \u2014 building a Tsetlin ecosystem and transitioning things that are neural networks into Tsetlin.\u201d<\/span><\/p>\n

But despite these challenges, Parkhouse-Parker believes Mignon\u2019s vision is very much achievable.\u00a0<\/span><\/p>\n

\u201cSeveral orders of magnitude improvement warrant a look at something that\u2019s new, novel, and exciting.\u201d<\/span><\/p>\n

The article you just read is from the premium edition of PreSeed Now<\/a>. This is a newsletter that digs into the product, market, and story of startups that were founded in the UK. The goal is to help you understand how these businesses\u00a0<\/em>fit into what\u2019s happening in the wider world and startup ecosystem.<\/em><\/p>\n<\/p><\/div>\n

Read More<\/a>
\n Martin SFP Bryant<\/p>\n","protected":false},"excerpt":{"rendered":"

This story is syndicated from the premium edition of PreSeed Now, a newsletter that digs into the product, market, and founder story of UK-founded startups so you can understand how they fit into what\u2019s happening in the wider world and startup ecosystem. The reignited excitement around the potential of AI as we hurtle into 2023<\/p>\n","protected":false},"author":1,"featured_media":603144,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25029,310,46],"tags":[],"class_list":{"0":"post-603143","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial","8":"category-doing","9":"category-technology"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/603143","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=603143"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/603143\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/603144"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=603143"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=603143"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=603143"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}