Giovanni Bricconi

The exhibit has been held between February 8th and February 10th in Cannes, French Riviera

While 2023 edition was highly democratized, this time the organization decided to allow access to the interesting speeches only to paying tickets. The company I work for has also bought less ticket so, sadly, I did not profit as much as I wanted of this event. The organization also changed the mobile app they had the last year: in 2023 was so easy to find what was going on across the exhibit, and this time it has become a mess: check each single room to see what was planned… I hope in 2025 they will change their minds.

One interesting speech I saw was “NeuroAI: Advancing Artificial Intelligence through Brain-Inspired Innovations”. The speakers described us some interesting research paths about building more efficient AI hardware. The human brain consumes a ridiculous amount of energy, comparable to a candle light, by converse the modern hardware is really energy-intensive, requiring hundreds of watts to operate. To make the things worst, the current trend is just to build bigger models using more hardware and more training data. It will be impossible to keep this trend, as the energy demanded will quickly become incredibly huge: 85 TWh by 2027 according to this scientific american article

More efficient hardware could probably mimic what the brain is doing: natural neurons fires at some incredibly low rate, just 30Hz. Neurons receive signals, and aggregate them producing an output only once a certain threshold is reached. The output spike does not last for long time and does not consumes that much energy.

It is possible to use some transistors to implement a similar behavior, this could become an unit that can be cloned million of times realizing in hardware an huge array of neurons. Training these devices wold then mean let them learn the correct thresholds and input connections. By converse current architectures require to continously compute values for each neuron output even if the output is not required.

One more energy-intensive task is moving the data around: current architectures have specialized memory units and computation units. The required data has to be moved in and out from the memory to the GPU and back. This requires a lot of energy.

IBM did some experiments, and seems using this neuromorphic patter gives some advantages over traditional LSTM architecture. You can visit Stanislaw Wozniak page to find some references.

I then followed some few other presentations, and some commercial demos. AMD’s one about realtime video processing was both appealing and frightening. You can buy now devices that can process video input streams at up of 90 frames per seconds. You can buy a software stack that allows you to view this camera flow and decide that on some video areas you want to perform some actions: for instance you put a camera watching a car access gate and decide that you use a region of the view to search for a car registration number. You could stack these operation on the remote device or turn you attention on a central system that now just receives events such has the car xx123yy has passed here at 12:05.

The guy setting up this stack of operation does not need to be a rocket scientist, a nice ui can help him setting up his/her video driven application. Moreover you could react to events and change the pipeline of some other cameras to perform other operations. In Singapore parkings facilities they are using this to detect free parkings and guide VIP customers to theirs places… or maybe to avoid non vip customers to pick reserved slots. So far so good, but what does prevent some malicious entity from having cams looking for a specif guy, and being reprogrammed to detect his activity while moving around a building? Do we have to fear that a cam at work place starts detecting how many times we go to the toilets? Unfortunately it does not seems anymore sci-fi… Field Proven Smart City Video Analytics Solution