Traditional artificial neural networks are an interconnected group of
The connections between these neurons are modelled as weights and all inputs to the
network are modified
based on these weights and summed to produce an outcome.
In contrast, SLIM™ can generate neural networks by making use of
just enough information (“See Less”),
which is selected intelligently by our algorithms, at a much faster rate.
By sensing only limited amount of the input data, taken from a camera or any sensor,
classification accuracy of 85% and 70% on MNIST and CIFAR, respectively, benchmark
Our ‘See Less. Infer More’ models also have a major impact on hardware
resource allocation, e.g. memory constrained devices.
SLIM™ can be deployed on cloud services, e.g. AWS or GCLOUD. These can also be
deployed on memory limitted and battery budgeted edge devices (e.g. mobile phones and
Our way of building models will also enable robots to see (i.e. detection,classification and localisation) in hazy, rainy or foggy environments.