Symbiotic Idemetric Processing
The Cortica architecture was designed to fit a client’s needs. It brings together three Intesym technologies: a massively-parallel Symbiotic processing architecture (Polymer) optimised for reconfigurable idemetric processing, interfacing to conventional computers via a SATAnet derivative interface to provide an on-line “Intelligent Storage” database device.
Cortica can be used for all of the functions to which Intesym idemetric processing is suited, such as image similarity, object recognition, and scene understanding.
Benefits of hardware implemention
Most image processing systems are implemented in software on workstations and servers. In many situations this is acceptable, but there are various situations where a custom hardware approach is preferable. For example:
- Hardware is more difficult to reverse-engineer.
- Hardware is more difficult to copy.
- Hardware is less susceptible to functional modification by tampering and hacking.
Efficiency & Reliability
- Custom hardware generally has lower power consumption for similar computational performance.
- Lower power allows passive cooling.
- Avoidance of moving parts such as fans improves system reliability.
- No inclusion of unused components and devices; less to go wrong.
Performance & Flexibility
- Hardware implementations of specialist functions are often considerably quicker than software versions even when there is an order of magnitude difference in clock rates.
- If a useful feature is slow in software, it can be implemented in hardware.
- Relatively quick and easy to adapt the hardware to suit new functions, even compared to many general processing software environments.
Benefits of Symbiotic processing
As Corticais a reconfigurable processor, there needs to be an element of programmability. Overall functionality is provided by software on a collection of Polymer Symbiotic processors. Custom circuitry is provided to accelerate important functions. For this kind of work, Symbiotic processing has numerous advantages over conventional CPU architectures:
- Neural and receptive field networks expressed directly, not simulated.
- Automatic propagation of data changes through networks without software coordination.
- Very efficient large-scale parallelism.
Benefits of SATAnet connectivity
Cortica appears as a hard disc to a host computer, and so access to it is simple through any operating system’s ordinary device drivers and filing-system calls. It can be installed internally in an enclosure’s standard 3.5″/5.25″ drive bay or connected externally via eSATA.
Cortica supports the processing of large full-colour (24-bit RGB) images and searching through large databases very quickly. The following table lists the preliminary specification for a typical Cortica-based image similarity database server.
|Query image size||1024 × 1024||pixels|
|Database size||1 000 000||images|
|Query throughput||25 †||images/sec.|
|Query latency||2.0 †||seconds|
|Power supply||9 ~ 24||volts D.C.|
|Power consumption||30||watts (max.)|
|Dimensions||145 × 100 × 25||mm (external)|
† For full-colour megapixel query images and a full database of one million full-colour megapixel images, returning similarity statistics against every database image for every query. Higher throughputs and lower latencies will occur for smaller images and/or smaller databases, or if only a subset of results (e.g. the best matches) is required.
Idemetrics is a very flexible technology and has many uses, including:
|Taxonomy||Identifying the species of plants and animals; discerning between spiral, barred, and elliptical galaxies.|
|Sorting||Assiging images to categories based on their content, such as people, cars, animals, landscapes.|
|Distribution||Determining the distribution of objects within a scene, such as locating and following a crowd.|
|Density||Determining the density or quantity of objects within a scene, such as how busy a street is, or measuring bacterial cultures.|
|Geography||Matching landscapes; identifying changes in landscapes.|
|Cartography||Matching OS-style maps and road-maps with aerial photographs.|
|OCR||Number-plate recognition; road-sign reading; font recognition; reading handwriting; spell-checking; interpreting sign-language.|
|Stylistic neutrality||Comparing images of similar things in different styles, such as matching photographs against sketches, clip-art, or “photo-fits”.|
|Image cleansing||Automatic removal of unwanted parts of an image (e.g. background stars in photographs of nebulae).|
|Scene understanding||Identifying objects; identifying objects within objects; identifying errors in a scene; identifying image faults.|