Most computing today utilises the energy contained in electrons to process data. Photonic computing replaces them with photons, using light waves to process and store data. Because the speed of light is unsurpassable, photonics provides a theoretical minimum latency and so makes light a better computing medium than electricity.

In principle, we could also observe a 10-50x bandwidth improvement over traditional computing due to the bits travelling at the speed of light. More, a 10x energy efficiency can be obtained with optical computing as one can increase the processing power without having to increase the electricity supply, which is the case for classical electronic computers.

CC’s innovation here is underpinned by our experience in developing photonic chips for medical imaging. We’re currently working on silicon photonics integrated circuits (PICs) for high speed and low latency and power consumption.

Encoding and storing information in biological cells is set to accelerate more sustainable and reliable computing. In 2020, a DNA storage machine developed by CATALOG, in partnership with Cambridge Consultants, achieved a landmark moment when it encoded the English-language Wikipedia into DNA – demonstrating success in terms of speed, scale and accuracy.

How does biological computing work? It uses biologically derived molecules such as DNA and proteins to perform. Advances in nanobiotechnology have led to the discovery of methods to program living cells to respond in predictable ways to certain chemical inputs. While this is costly and labour intensive in the first instance, it then becomes extremely cost-effective to grow billions more.

As our example proves, writing information into DNA makes it possible to store vast amounts of data very inexpensively in a smaller and passive form – reducing energy cost in comparison to silicon. As the energy used by data centres doubles every four years, biological computing promises a more sustainable future.

Quantum computing is based on the physical phenomena where electrons can simultaneously behave as material particles or waves. This property changes the basic idea of storing and processing information in the form of binary bits (0 or 1) and instead using qubits (any number from 0 to 1). Particles will be able to represent any of an infinite number of states and interact with other particles in infinite different ways.

Quantum computers have the potential of solving very complex problems faster than traditional computers. They represent astonishing commercial promise, and we are currently advising clients on how to begin road-mapping their strategies for commercial adoption. From exploration comes understanding comes pioneering use cases – and valuable first-mover, protectable advantage.

The emergence of neuromorphic computers – inspired by biology and mimicking the neural systems of the human brain – promises extraordinary performance and energy efficiency. Neuromorphic computing is ideally suited to low-power edge AI applications. We anticipate it will help unlock all kinds of valuable new commercial applications, from smart vision systems to autonomous robots.

Neuromorphic computers don’t use sequential data processing like traditional computers. They imitate how neurons process information, which are stimulated through spikes that trigger one or multiple neurons. These then activate others. The approach uses artificial silicon neurons to form a spiking neural network (SNN) that performs event-triggered computation. This enables parallel and targeted processing for a defined set of neurons and interconnections.

Ultra-low power operation is achieved thanks to SNNs being in an ‘off’ mode most of the time and only kicking in when a change, or ‘event’, is detected. Once in action, fast computation is achieved without running an energy consuming fast clock speed by triggering a huge number of parallel operations (equivalent to 1000s CPU in parallel). So, only a fraction of power is consumed compared to CPU/GPU for the same workload.