Neuromorphic technology: a role in the workplace

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By Prof Pierre Brunswick, CEO, NeuroMem

AI’s role in the workplace is just starting and there is a long journey ahead of us.

Today, there are still very few businesses investing in developing AI-based solutions. When they do, the focus seems to be on one aspect of AI – deep learning, an idea using the Perceptron classifier that was originally introduced in the 1980s but had to be abandoned due to lack of computing technology and lack of big data and the cloud, now enjoying a resurgence.

On the other hand, cognitive systems based on neuromorphic technology, which has also been around since late 1980s developed by companies like General Vision, are probably a better engine for businesses because they consume low power and offer non-stop learning. This also means that because it focuses on anomalies at the source, it uses less resources including data, cloud and internet, creating a lower cost of operations and is much faster.

I truly believe that non stop learning, and pattern recognition offered by neuromorphic technology can become practical and ubiquitous only if it can rely on components inspired by the human brain (neuromorphic memories), merging storage and local processing per cell, with massively parallel interconnected cells operating at low power. It also vastly reduces the time to market for businesses planning to implement robust AI solutions at a hardware level.

There is a need for more education to empower businesses to adopt AI faster and stay ahead of the competition. Proponents of neuromorphic technology, like us, are working with developers of a wide range of applications across the globe. In the hardware-based sector, there is a drive on the integration of AI in smart buildings, smart infrastructure, security, medical and related applications, automotive sector, drones, IoT and robotics – anywhere that uses sensors to filter information to make business decision, securely and cost effectively.

Neuromorphic technology also excels at commonly-used iris and face recognition. The neurons detect faces without having to identify them as an individual, which cuts down on the need for personal information in its functioning. Using parallel architecture allows the massive scalability of the product makes it usable in crowded areas like metros and airports.

If businesses want to stay competitive, they need to start investing in tools and collaborate with organisations that can help them develop Proof of Concept (POC) solutions based on their customised needs. There is no one size fits all solution when it comes to AI.

About the author:

Professor Dr. Pierre Brunswick is the CEO of NeuroMem Technologies, based in Singapore. He brings over 40 years of international experience in business development, sales, marketing, engineering, finance and incubators to his role of CEO at Neuromem. Throughout his career, he has managed a region covering 94 countries and 9 local offices. He has helped customers complete large projects and implement the right strategy to grow, merge, acquire new businesses, go public, hire the necessary talent and establish local offices as well as form joint ventures.

About NeuroMem Technologies:

NeuroMem Technologies, a pattern recognition technology company focused on the use and benefits of neuromorphic semiconductor components, is licensing NeuroMem chips and technology, which will allow everyday objects to have perception of their environment and interact with users. We truly believe that pattern learning and recognition can become practical and ubiquitous only, if it can rely on components inspired by the human brain, which we call neuromorphic memories (1) merging storage and local processing per cell, and (2) massively parallel interconnected cells operating at low power.

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