HYPERDIMENSIONAL COMPUTING: A NEW APPROACH

An innovative method of computing called hyperdimensional computing that seeks to imitate how the human brain functions was first published in 2016 by a group of researchers of the University of Oxford.

In hyperdimensional computing, information is represented using high-dimensional vectors, which often have several hundred dimensions or more. Simply put, a vector is a sorted array of numbers. The x, y, and z coordinates of a point in three dimensions make up a 3D vector, for instance, which consists of three values.

An array of 10,000 values, for example, could represent a point in a 10,000-dimensional space and be referred to as a hyperdimensional vector or hyper vector. These mathematical objects, together with the algebra used to handle them, are adaptable and potent enough to push the boundaries of current computers and encourage a fresh view on AI.

These vectors are frequently built using random values, which are then merged using mathematical operations like addition, subtraction, and multiplication to produce intricate patterns.

The capacity to carry out specific types of computations utilizing these high-dimensional vectors is one of the main benefits of hyperdimensional computing. Consider the scenario when you need to identify a specific pattern, such as a handwritten digit.

In conventional computers, this would entail comparing the input image's pixel values to a database of well-known digit patterns.

However, in hyperdimensional computing, you might create a high-dimensional vector to represent each digit and compare it to the input image to determine how similar they are. Compared to conventional pattern recognition methods, this can be a lot faster and more effective. A variety of methods have been investigated to implement hyperdimensional computing on low-power devices.

Utilizing analog hardware, such as memristors, which can carry out vector-based operations directly in hardware, is one strategy.

Another strategy is to employ digital gear, but to keep the computations as straightforward as feasible. For instance, rather than using more complicated circuits, you may do the vector operations using straightforward logic gates.

In some circumstances, hyperdimensional computing may be less power-hungry than conventional computing.

This is due to the fact that hyperdimensional computing relies on basic mathematical operations like addition, subtraction, and multiplication, which can be performed using hardware that is more straightforward and compact than the intricate logic gates used in traditional computing.

Additionally, compared to conventional computing, the quantity of data that needs to be saved and processed in hyperdimensional computing might be considerably less because it uses high-dimensional vectors to represent and manipulate data.

As a result, hyperdimensional computing systems may consume less power overall which is critical in our present energy transition times.

The next step is the fusion of quantum computing and hyperdimensional computing.

In order to conduct hyperdimensional computing tasks more quickly and precisely, it is intended to take advantage of several special qualities of quantum computing, such as superposition and entanglement.

Performing vector operations on qubits using quantum circuits is one conceivable method for quantum hyperdimensional computing.

In comparison to conventional computers, this will result in more effective and quicker computing of high-dimensional vectors.

Quantum hyperdimensional computing may also be employed in ML, where hyperdimensional models may be trained and improved using quantum methods. For tasks like pattern recognition and classification, this might result in more precise and effective ML models.

Hyperdimensional computing may be combined with AI systems to carry out a variety of tasks.

A framework for describing and manipulating data is provided by hyperdimensional computing, and AI systems can be used to identify patterns in the data and make predictions or judgments based on those findings.

An AI algorithm might be trained to spot patterns in high-dimensional vectors that represent photographs, for instance, and then employ hyperdimensional computing operations to categorize brand-new images using the patterns it has discovered. In a similar vein.

AI systems might be used to identify patterns in high-dimensional vectors that reflect natural language content and then produce summaries or responses using hyperdimensional computing operations.

Additionally, hyperdimensional computing will be used to create AI models that are more effective and precise.

In hyperdimensional computing, high-dimensional vectors are used to represent data in a more effective and expressive manner. This can help to lower the volume of training data i.e. less power-hungry that is needed to increase the precision of the resulting AI models.

In conclusion, AI algorithms will be a crucial tool when used with hyperdimensional computing, assisting in the discovery of patterns in highly dimensional data and the subsequent generation of predictions or judgments based on those patterns.

On the other hand, hyperdimensional computing will be a useful tool for creating AI models that are more precise and effective.

Thebestofbothworlds…

Oranew “Godzilla”inthemaking?