The Deep Dive: Neuromorphic Computing – The Next Big Thing

Computing has seen many paradigm shifts: mainframes to desktop, client-server to web, on-premise to cloud. Each coming with its own power of form, ownership and business model. Neuromorphic computing, one that is inspired by the structure and function of the brain, represents another shift, one that holds the possibility of more efficient, adaptive and intelligent computing systems.

Source of this above image: https://www.nature.com/articles/s43588-021-00184-y

Advantages and disadvantages carry considerable weight across the economic, social, environmental and technological spectra. Let’s start with some of the challenges before moving on to the advantages.

Advantages

Technological Efficiency and Speed

The human brain exhibits many characteristics that make it incredibly efficient and fast. There are a couple of examples that are particularly exciting.

- Energy Efficiency: Because neuromorphic chips only use energy when they are performing calculations, unlike traditional CPUs or GPUs which use energy even when they aren’t, they can operate at much lower power levels. This is great for mobile devices and sensors because it increases battery life and reduces electricity consumption in data centers.

- Processing Speed: Neuromorphic computers can process complex, unstructured data from sensors and other inputs much more quickly than traditional processors. They can execute tasks like decision making and pattern recognition many times faster than conventional systems.

- The development of neuromorphic computing technologies may spur innovation and lead to new products and services, potentially opening new markets and driving economic expansion.

- As neuromorphic systems grow more energy-efficient and less costly to operate over time, there could be potential cost reductions for industries and consumers, which could lead to economic efficiency overall.

- By advancing artificial intelligence (AI) and machine learning, neuromorphic computing can allow for more advanced and adaptable models. Potential applications of such technology are vast and include everything from advanced diagnostic tools in healthcare and specially tailored lessons in education to highly personalized services and more leisure time, which could all ultimately lead to enhanced quality of life.

- If neuromorphic computing enables more efficient processing at the mobile level and on the edge, this may allow that much more advanced capabilities to filter down to remote or underserved areas, mitigating differences in access to advanced computing capabilities.

- As mentioned earlier, the energy efficiency of neuromorphic computing has a direct positive impact on the environment, in that there are lower carbon emissions for the energy requirements from electricity production for computing tasks.

- The potential for neuromorphic systems run run on much less power could further push the revolution in renewable energy. This sustainable computing could open up computing to use renewable energy as well as reducing the overall power requirements for many compute-intensive tasks, possibly spurring a new generation of sustainable computing practices.

Disadvantages

Technological Challenges

- Complexity in Development: The development of neuromorphic chips which can replicate the complex functions of the brain is a highly complex technology and will require significant research and development.

- Integration with Existing Systems: Integrating neuromorphic computing with standard computing infrastructure will also require significant research and will likely take time. This might slow its adoption, and it will have to be backward-compatible with existing systems, or we will have to upgrade everything. That said, if it is to prove itself commercially viable, adapting the latest and greatest neuromorphic chip will probably be a lot more cost efficient than replacing your standard computer every time a new generation is released.

Economic Impacts

- High Initial Costs: Developing and producing neuromorphic computing systems will involve significant upfront costs. This could make them accessible only to large corporations, or research institutions, and limit their use within homes or small businesses. This could potentially widen the digital divide.

- Disruption of Job Markets: Just as with any significant technological advancement, neuromorphic computing could see huge disruptions of job markets. Particularly, it is expected that neuromorphic computing will have significant impacts on the IT and intelligence industries, which will both become far less reliant on traditional computing and data analysis roles.

Social Impacts

- Ethical and Privacy Concerns: Neuromorphic computing’s advanced capabilities in analyzing and processing data has created concerns around privacy and the potential for abuse of these abilities. Thinking about the issues surrounding surveillance, personal profiling, and data mining, it is clear that there will also have to be development on the laws surrounding the technology to prevent these from being abused.

- Dependency and Security: As with all advances in algorithmic processing, our engagement with this algorithm has the potential to reveal a vast new set of vulnerabilities. With these vulnerabilities laid wide open at the TR17 Summit, it is clear that they need to be addressed immediately.

Dependency on such systems would have huge tracking capabilities. If the product is compromised, it could lead to several security risks both for a governments critical infrastructure and personal devices.

Environmental Impacts

- E-Waste: Finally, the transition to neuromorphic computing could lead to e-waste as existing hardware becomes obsolete. The environmental and ecological impact of manufacturing and disposing of neuromorphic hardware should be taken into account as well.

It’s clear that the transformational potential of neuromorphic computing across technological, economic, and social spheres comes with significant challenges. We’ll have to balance this promise against these problems as we continue to both think about the implications of this work and develop new policy and technology.