A brain-inspired computer chip can run AI-powered image recognition operations 22 times faster than comparable commercial chips, and with 25 times the energy efficiency. The IBM NorthPole chip intertwines its computational capability with associated memory blocks that store information. This allows it to bypass the so-called the von Neumann bottleneck – named after computing pioneer John von Neumann – which describes how modern computers slow down while waiting on information exchanges between more separated compute and memory units. The melding of computation and memory was inspired by the way the human brain works. IBM had previously built a chip based on this idea called TrueNorth. But NorthPole transforms the technology into a digital architecture that is compatible with the silicon chip technology used in contemporary computers.

Artificial intelligence algorithms cannot keep growing at their current pace. Algorithms like deep neural networks -- which are loosely inspired by the brain, with multiple layers of artificial neurons linked to each other via numerical values called weights -- get bigger every year. But these days, hardware improvements are no longer keeping pace with the enormous amount of memory and processing capacity required to run these massive algorithms. Soon, the size of AI algorithms may hit a wall. And even if we could keep scaling up hardware to meet the demands of AI, there's another problem: running them on traditional computers wastes an enormous amount of energy.

In recent years, many research teams worldwide have been developing computational techniques inspired by the human brain, such as deep learning algorithms. While some of these techniques are considered highly promising for a wide range of applications, conventional hardware does not always support their computational load and thus can limit their performance. A possible solution for overcoming the limitations of existing hardware and ensuring that brain-inspired computational techniques achieve optimal results entails the creation of new electronic components that better reflect the structure of the human brain. A class of neuro-inspired computing chips is specifically designed for artificial intelligence (AI) applications that mimic the neural structures in the brain of humans and other animals. Researchers at Tsinghua University in China have reviewed recent advancements in the design of neuro-inspired computing chips to gain insight on the progress made so far and identify challenges that still need to be overcome.

"The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe," Michio Kaku, Physicist and Futurist The human brain, which not just stores but also computes, is by far the most powerful and complex computers in the world that occupies just 1.3 litres of space and consumes about 20 watts of power. In comparison, the finest supercomputers in the world require gigawatts of power, massive real estate, infrastructure, and dedicated cooling systems while attempting to perform brain-like tasks. Understanding how the human brain functions and replicating it has been a lifelong quest for the scientific and research community. Enter neuromorphic computing, a concept developed by American scientist and researcher Carver Andress Mead in the late 1980s – which tries to emulate certain functions of the human brain in silicon.

Satellites, aircraft, and growing numbers of drones--the U.S. Air Force has a lot of electronic eyes in the sky. Now it's exploring whether brain-inspired computer chips could give those systems the smarts to do things like automatically identify vehicles such as tanks or anti-aircraft systems. The Air Force Research Lab (AFRL) reports good results from using a "neuromorphic" chip made by IBM to identify military and civilian vehicles in radar-generated aerial imagery. The unconventional chip got the job done about as accurately as a regular high-powered computer, using less than a 20th of the energy. The AFRL awarded IBM a contract worth $550,000 in 2014 to become the first paying customer of its brain-inspired TrueNorth chip. It processes data using a network of one million elements designed to mimic the neurons of a mammalian brain, connected by 256 million "synapses."