Applying Cognitive Memory to Pattern Recognition

CogniMem can be considered a true Artificial Intelligence device because it has been designed specifically for the purpose of learning, memorizing and recognizing.

What Are Intelligent Systems?

An intelligent system can get a basic training (instead of programming) to react to its operating environment. It should then be able to adapt and report to fast or slow changes occurring in this environment. Indeed, this implies perception ability. We can postulate that intelligence pertains more to active memories reacting to incoming stimulus than a processor endlessly playing a programmed “recipe”.

It was well described in Jeff Hawkins’s landmark book, On Intelligence, that an essential difference between the biological approach and the computing approach to intelligence lays in the fact that biology uses active memory cells (neurons) while computers use a procedural activity involving the “fetch, decode and execute” model. Intelligence is also about adaptive learning, whether it is supervised or not. This implies that the memory cells can snoop the response of other cells before making a decision to learn a new model or change the confidence level with which they recognize an existing model. This process has to occur in real-time and not be affected by the number of connected cells

Pattern Recognition Challenges

First, let’s describe some of the challenges faced in “real world” image applications or signal identifications. There are at least three:

1. Acquisition stability and noise problems;
2. Pattern characterization and data reduction process;
3. Accuracy and speed of the classifier (decision making component).

Real world patterns are subject to noise and jitter. For example a visual object such as a mug in a scene will have many variations. Homogeneous lighting variation will change the overall contrast in an image. This kind of variation can be dealt with using the well-known normalized correlation. Light reflecting on the shiny cup will actually change the apparent shape and, therefore, cannot be recognized with standard methods. Inspection of “nature made” products is another example of difficult images to deal with. While two herrings can look identical to a human eye, their digitized images might belong to different computed models. Recognizing a large population of these fishes will involve building a statistically viable model base. This model set can be very large and, therefore, a standard computer going through these models sequentially will need hefty computation capability, leading to high consumption, heating issues, and large footprint.

Performance should be defined through speed, footprint, power consumption, real-time learning and non-linear classification capabilities. High frequency clock devices with a single fetch and decode operation do not run parallel processes efficiently. While the concept of trainable neural networks has been known for decades, and a fair amount of software drawbacks exhibited, very few hardware implementations have reached the industry with absolute digital parallelism. Today, the CogniMem chip (CM1K) offers a very efficient alternative to sequential processors such as RISC or DSP’s for near sensor image, signal or parameters recognition.