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IBM Phase-Change Device Imitates Functionality of Neurons

IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.

IBM TrueNorth Project wins Inaugural Misha Mahowald Prize

The inaugural Misha Mahowald Prize for Neuromorphic Engineering has been awarded to the TrueNorth project, led by Dr. Dharmendra S. Modha at IBM Research. “The Misha Mahowald Prize recognizes outstanding achievement in the field of neuromorphic engineering. Neuromorphic engineering is defined as the construction of artificial computing systems which implement key computational principles found in natural nervous systems. Understanding how to build such systems may enable a new generation of intelligent devices, able to interact in real-time in uncertain real-world conditions under severe power constraints, as biological brains do.”

Video: Will AI & Robotics Make Humans Obsolete?

“IBM has developed new scale-up and scale-out systems — with 16 million neurons — that will be presented in Dr Modha’s pioneering research talk. Watson wins at Jeopardy and enters industrial applications while AlphaGo defeats the human Go champion. No day goes by before the dooms day prediction of AI infused Robots taking over our world comes up in the news. Visions of HAL and Terminator coming alive? Will Artificial Intelligence make us obsolete?”

Video: Neuromorphic Computing – Extreme Approaches to Weak and Strong Scaling

“Computer simulations of complex systems provide an opportunity to study their time evolution under user control. Simulations of neural circuits are an established tool in computational neuroscience. Through systematic simplification on spatial and temporal scales they provide important insights in the time evolution of networks which in turn leads to an improved understanding of brain functions like learning, memory or behavior. Simulations of large networks are exploiting the concept of weak scaling where the massively parallel biological network structure is naturally mapped on computers with very large numbers of compute nodes. However, this approach is suffering from fundamental limitations. The power consumption is approaching prohibitive levels and, more seriously, the bridging of time-scales from millisecond to years, present in the neurobiology of plasticity, learning and development is inaccessible to classical computers. In the keynote I will argue that these limitations can be overcome by extreme approaches to weak and strong scaling based on brain-inspired computing architectures.”

IBM & LLNL Collaborate on TrueNorth Neuromorphic Computing

Today Lawrence Livermore National Laboratory (LLNL) announced it has purchased a first-of-a-kind brain-inspired supercomputing platform for deep learning inference developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a tablet computer – a mere 2.5 watts of power for the 16 TrueNorth chips. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips.