The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

« How Covid-19 is shaping the businesses of tomorrow | Main | Learning to Trust Synthetic Data... »

Neuromorphic Computing - Next generation of AI

The 1st generation of artificial intelligence drew conclusions based on classical logic in a specific defined problem and was useful to monitor processes and improve efficiency. The 2nd generation is concerned with sense and perception, like analyzing video frame content using deep learning networks.

The next gen AI will move towards human cognition, with qualities like adaptation and interpretation. It will overcome the limitation of AI solutions whose training depends on deterministic views of events and lack context. Next-generation AI will emulate ordinary human activities. Hence, neuromorphic computing comes into the picture.

Traditional computing is reaching its limit are becoming inefficient to handle the next wave of AI. With Moore's Law (the number of transistors doubles every two years while the cost halves) almost reaching its limit, there is a search for new paths to increase the computational abilities to take AI to the next level.  Traditionally, all computers are based on Von Neumann architecture where memory and processor are isolated and data moves between them. This is different from biological computers, i.e. brain where the memory and logic are closely connected in neurons and signals are transmitted through synapses.  

A neuromorphic chip copies this model by implementing neurons in silicon with a goal to impart cognitive abilities to machines. This dense network on neuromorphic chips is called Spiking Neural Network (SNN). This network encodes information in form of spike trains, i.e., time difference between two spikes determines network properties. Neuron functioning is governed by differential equation and uses analog signals exchanging electric signal bursts at different intensities. It has an event-driven nature of only making neurons in action active. This is unlike the current digital chips which are binary based and have continuous values. Due to this uniqueness, the SNN has a different training method than Artificial Neural Network (ANN). It uses Spike Time Dependent Plasticity (STDP) rather than gradient descent.

Connected processor and memory makes neuromorphic chips more efficient at training and running neural networks. They run AI models faster than equivalent CPUs and GPUs while consuming less power. This is crucial as power consumption is a huge challenge for AI. The small size and low power consumption also make them suited for use cases that require running AI algorithms at the edge as opposed to the cloud. Neuromorphic computing can create algorithmic approaches to deal with uncertain and ambiguous situations.

A lot of big companies like IBM, Intel, Qualcomm have become key players in the space. Intel designed Loihi chip, which contains 131,000 neurons and 130 million synapses and processes information up to 1,000 times faster and 10,000 more efficiently than traditional processors. Qualcomm created Zeroth chip which uses deep learning in a low-power platform suited for cell phones. IBM's TrueNorth chip has over a million neurons and over 268 million synapses. It is 10,000 times more energy-efficient than conventional microprocessors and only uses power when necessary.

Neuromorphic computing has various application segments such as image processing, signal processing, object detection, data processing.  

In self-driven and smart vehicles, it will help in sensing and responding erratic behavior of surrounding vehicles. It will also play a role in satellites for surveillance and aerial imagery. Other applications include healthcare monitoring ,smart spaces and cyber-security.   

For enterprises, this technology could mean massive improvements in a host of areas, from predictive data analytics to automation and process optimization.

Overall, neuromorphic computing provides a highly probable solution to the upcoming performance crisis.

Post a comment

(If you haven't left a comment here before, you may need to be approved by the site owner before your comment will appear. Until then, it won't appear on the entry. Thanks for waiting.)

Please key in the two words you see in the box to validate your identity as an authentic user and reduce spam.

Subscribe to this blog's feed

Follow us on