NeuroMem® Technologies Pte Ltd, is licensing NeuroMem IP, which will allow everyday objects to have perception of their environment and interact with users. We truly believe that pattern learning and recognition can become practical and ubiquitous only, if it can rely on components inspired by the human brain, which we call neuromorphic memories merging storage and local processing per cell, and massively parallel interconnected cells operating at low power. For more information please contact Calvin Ng, Director of Licensing
Neuromorphic Technology Available for Licensing
The NeuroMem IP is a bank of neuromorphic memories, also considered as a neural network, capable of learning and recognizing patterns at high speed and low-power. The patterns can derive from any data types such as text, scientific datasets, bio-signals, audio files, images and videos, etc. Thanks to a unique parallel architecture, the neurons can learn and recognize a pattern in a constant amount of time regardless of the number of reference models they hold.
The NeuroMem® technology has been growing and improving patiently waiting for the market to be ready for its acceptance. Today, the trendy IoT and Big Data are steering the demand for high-speed, low-power pattern recognition and machine learning tools and we are ready with a commercial chip on the shelf, IP available for licensing, and the pleasure of knowing that real-time adaptive learning, also called life-long learning, is the sole privilege of the biology and NeuroMem.
Pattern matching is everywhere. As applications in IoT, context awareness and autonomous vehicles begin to see broader adoption the need for fast, efficient, low power matching is becoming critical.
Current pattern matching techniques, based on traditional processing cores like DSPs, CPUs, and GPUs take the pattern and search, one at a time, through a list of possible patterns. This presents a fundamental problem when you have to search across a large database of potential matches. The time and power it will take to make decisions using conventional approaches are quickly becoming unpractical.
The fundamental core of the technology is based on massively parallel pattern matching capability. The time it takes to match a pattern to a single trained neuron can be the same is it would take to match against a million neurons, currently a fraction of a microsecond. Our technology includes the ability to realize that a pattern has not been trained and represents novelty, which can be learned on the fly, while also continuing to operate. This automatic generation of models is done without the need to write any software to support the learning. The techniques are based on how the brain learns by associating stimuli to an output which can be a name, an action or experience. As a child you saw, heard, or felt something, and someone told you what that thing was. You stored the pattern in your memory and the next time you encountered it you recalled what it was. The true benefit of learning by examples is that anyone can transfer his knowledge by creating a training set, from a person working on a factory floor to assure top-notch quality products, to someone adding deep layers of personalization on their phone or smartwatch.