Just a couple of months ago DancingDinosaur reported a significant IBM advance in phase change memory (PCM). Then earlier this month IBM announced success in creating randomly spiking neurons using phase-change materials to store and process data. According to IBM, this represents a significant step toward achieving energy-efficient, ultra-dense, integrated neuromorphic technologies for application in cognitive computing.
Phase Change Neurons
This also represents big step toward a cognitive computer. According to IBM, scientists have theorized for decades that it should be possible to imitate the versatile computational capabilities of large populations of neurons as the human brain does. With PCM it appears to be happening sooner than the scientists expected. “We have been researching phase-change materials for memory applications for over a decade, and our progress in the past 24 months has been remarkable,” said IBM Fellow Evangelos Eleftheriou.
As the IBM researchers explain: Phase-change neurons consist of a chip with large arrays of phase-change devices that store the state of artificial neuronal populations in their atomic configuration. In the graphic above individual devices are accessed by means of an array of probes to allow for precise characterization, modeling and interrogation. The tiny squares are contact pads that are used to access the nanometer-scale, phase-change cells (not visible). The sharp probes touch the contact pads to change the phase configuration stored in the cells in response to the neuronal input. Each set of probes can access a population of 100 cells. The chip hosts only the phase-change devices that are the heart of the neurons. There are thousands to millions of these cells on one chip that can be accessed (in this particular graphic) by means of the sharp needles of the probe card.
Not coincidentally, this seems to be dovetailing with IBM’s sudden rush to cognitive computing overall, one of the company’s recent strategic initiatives that has lately moved to the forefront. Just earlier this week IBM was updating industry analysts on the latest with Watson and IoT and, sure enough, cognitive computing plays a prominent role.
As IBM explains it, the artificial neurons designed by IBM scientists in Zurich consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one (without a clearly defined structure) and a crystalline one (with structure). These artificial neurons do not store digital information; they are analog, just like the synapses and neurons in our biological brain, which is what makes them so tempting for cognitive computing.
In the published demonstration, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallization of the phase-change material, ultimately causing the neurons to fire. In neuroscience, this function is known as the integrate-and-fire property of biological neurons. This forms the foundation for event-based computation and, in principle, is similar to how our brain triggers a response when we touch something hot.
Even a single neuron can exploit this integrate-and-fire property to detect patterns and discover correlations in real-time streams of event-based data. To that end, IBM scientists have organized hundreds of artificial neurons into populations and used them to represent fast and complex signals. Moreover, the artificial neurons have been shown to sustain billions of switching cycles, which would correspond to multiple years of operation at an update frequency of 100 Hz. The energy required for each neuron update was less than five picojoule and the average power less than 120 microwatts (for comparison, 60 million microwatts power a 60 watt lightbulb).
The examples the researchers have provided so far seem pretty conventional. For example, IoT sensors can collect and analyze volumes of weather data collected at the network edge for faster forecasts. Artificial neurons could be used to detect patterns in financial transactions that identify discrepancies. Even data from social media can be used to discover new cultural trends in real time. To make this work, large populations of these high-speed, low-energy nano-scale neurons would most likely be used in neuromorphic coprocessors with co-located memory and processing units, effectively mixing neuron-based cognitive computing with conventional digital computing.
Makes one wonder if IBM might regret spending millions to dump its chip fabrication capabilities. According to published reports Samsung is very interested in this chip technology and wants to put the new processing power to work fast. The processor, reportedly dubbed TrueNorth by IBM, uses 4,096 separate processing cores to form one standard chip. Each can operate independently and are designed for low power consumption. Samsung hopes the chip can help with visual pattern recognition for use in autonomous cars, which might be just a few years away. So, how is IBM going to make any money from this with its chip fab gone and commercial cognitive computers still off in the future?
DancingDinosaur is Alan Radding, a veteran information technology analyst and writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.
Tags: analytics, artificial neurons, chips fabrication, cognitive computing, digital computing, germanium antimony telluride, IBM, integrate-and-fire property, IoT, nanometer-scale, neuromorphic coprocessors, neurons, neuroscience, Phase Change Memory PCM, Samsung, technology, Watson