The average human adult brain weighs about three pounds and is comprised mostly of fat and water, but it is extremely efficient at processing information. To simulate just one second of biological brain activity several years ago, researchers used 82,994 processors, one petabyte of system memory and 40 minutes on the Riken Research Institute's K supercomputer. At the time, this system consumed enough electricity to power about 10,000 homes. In contrast, the brain uses the equivalent of about 20 watts of electricity—barely enough to power a dim light bulb.
The human brain is also much better than computers at tasks like recognizing images and navigating unfamiliar spaces. Although the precise mechanism by which the brain performs these tasks is still unknown, it is known that visual information is processed in a massively parallel and concerted fashion by millions of neurons connected by synapses. Each neuron responds to visual stimuli in a simple, on-demand fashion, but their collective responses can yield cognitive outcome that currently cannot by easily described by a simple mathematical model. These models are essentially the foundation of current image processing software executed on traditional computing systems. All computing systems since the 1940s — from smartphones to supercomputers — have been built from the same blueprint, called the von Neumann architecture, which relies on mathematical models to execute linear sequences of instructions.
The von Neumann design has also led computing to its current limits in efficiency and cooling. As engineers built increasingly complex chips to carry out sequential operations faster and faster, the speedier chips have also been producing more waste heat. Recognizing that modern computing cannot continue on this trajectory, a number of companies are looking to the brain for inspiration and developing "neuromorphic" chips that process data the same way as human minds. One such technology is IBM's TrueNorth Neurosynaptic System.
Although neuromorphic computing is still in its infancy, researchers in the Computational Research Division (CRD) at the U.S. Department of Energy's Lawrence Berkeley National Laboratory hope that these tiny, low-power, brain-inspired computing systems could one day help alleviate some of science's big data challenges. With funding from the Laboratory Directed Research and Development (LDRD) program, two groups of researchers are exploring how science might benefit from this new technology.
One group of CRD researchers is looking at how neuromorphic chips might be able to provide low-power, real-time data processing for charged particle tracking in high energy physics experiments and prediction of movement from neural signals for brain machine interfaces. So they are working to implement Kalman filters on TrueNorth chips, effectively expanding the utilization of this neuromorphic technology to any computing problem benefiting from real-time, continuous tracking or control.
Meanwhile, another collaboration of researchers from CRD and the Molecular Biophysics and Integrated Bioimaging (MBIB) division looked at the viability of applying convolutional neural networks (CNNs) on IBM's TrueNorth to classify images and extract features from experimental observations generated at DOE facilities. Based on their initial results, the team is currently working to identify problems in the areas of structural biology, materials science, and cosmology that may benefit from this setup.
"The field of neuromorphic computing is very new, so it is hard to say conclusively whether science will benefit from it. But from a particle physics perspective, the idea of a tiny processing unit that is self-contained and infinitely replicable is very exciting," says Paolo Calafiura, software & computing manager for the Large Hadron Collider's ATLAS experiment and a CRD scientist.
"For one reason or another — be it I/O, CPU, or memory — every computing platform that we've come across so far hasn't been able to scale to meet our data processing needs," Calafiura says. "But if you can replicate the same tiny unit of processing 10 million times or more, as neuromorphic computing aims to do, and find the right balance between power consumption and processing speed, this sounds like it will meet our needs."
In the traditional von Neumann design, computers are comprised primarily of two components: a CPU that handles data, and random access memory that stores data and the instructions for what to do with it. The CPU fetches its first instruction from memory, and then data needed to execute it. Once the instruction is performed, the result is sent back to memory and the cycle repeats.
Rather than go back and forth between CPU and memory, the TrueNorth chip is a self-contained computing system in which processing units and memory are co-located. Each chip contains 4,096 neurosynaptic cores that contain 1 million programmable neurons and 256 million configurable synapses interconnected via an on-chip network. The neurons transmit, receive, and accumulate signals known as spikes. A neuron produces a spike whenever accumulated inputs reach a programmed activation threshold. They are weighted and redirected by synapses that connect different layers of neurons to map input to output.
TrueNorth chips natively tile in two dimensions using the on-chip network, essentially allowing the system to seamlessly scale to any size. Because synapses serve a dual function of memory and CPU, neuromorphic chips pack a lot of computing power into a tiny footprint and use significantly less power. For instance, TrueNorth uses about 70 milliwatts of electricity while running and has a power density of 20 milliwatts per square centimeter — almost 1/10,000th the power of most modern microprocessors.
"Low-energy consumption and compact size are some of the reasons we're interested in neuromorphic computing," says Chao Yang, an applied mathematician in Berkeley Lab's CRD. "With these miniature computing systems, we expect that soon we will enable scientific instruments to be more intelligent by doing real-time analysis as detectors collect information."
According to CRD scientist Daniela Ushizima, incorporating these neuromorphic chips into detectors could mean huge computational savings for imaging facilities. Rather than send raw data directly to a storage facility and then figure out post-acquisition whether the information collected is relevant, good quality or includes the object of interest, researchers could just do this exploration in situ as the data is being collected.
The size of the chips also presents new possibilities for wearables and prosthetics. "In our time-series work, we're exploring the potential of this technology for people who have prosthetics implanted in their brains to restore movement," says Kristofer Bouchard, a Berkeley Lab computational neuroscientist. "While today's supercomputers are powerful, it is not really feasible for someone to tote that around in everyday life. But if you have that same computing capability packed into something the size of a postage stamp, that opens a whole new range of opportunities."
Because neuromorphic chips are vastly different than today's microprocessors, the first step for both projects is to translate the scientific methods developed for modern computers into a framework for the TrueNorth architecture.
In the Particle Physics and Brain Machine Interfaces research project, co-leads Kristofer Bouchard and Paolo Calafiura are working to set up their scientific framework on the TrueNorth architecture. They implemented Kalman filters using IBM TrueNorth Corelet Programming Language and they explored strengths and weaknesses of the various TrueNorth's transcoding schemes that convert incoming data into spikes. Once fully tested, this TrueNorth Kalman filter will be broadly applicable to any research group interested in sequential data processing with the TrueNorth architecture.
"As these transcoding schemes have different strengths and weakness, it will be important to explore how the transcoding scheme affects performance in different domain areas. The ability to translate any input stream into spikes will be broadly applicable to any research group interested in experimenting with the TrueNorth architecture," says Calafiura.
Once the team has successfully set up their workflow on TrueNorth, they will train their spiking neural network Kalman filters on real neural recordings taken directly from the cortical surface of neurosurgical patients. The team will also train their implementations with high energy physics data collected at the Large Hadron Collider in Geneva, Switzerland and Liquid Argon Time Processing Chambers at FermiLab.
Companies like Google and Facebook are using convolutional neural networks (CNNs) to identify and categorize faces, locations, animals, etc., using billions of images uploaded to the Internet every day. Users essentially help "train" these CNNs every time they tag a location or friend in a picture.
Because CNN designs evolved from early research of the brain's visual cortex and how neurons propagate information through complex cell organizations, the co-leads of the "Image Analysis and Pattern Recognition" project — Chao Yang, Nick Sauter, and Dani Ushizima — thought that this algorithm might be a good fit for neuromorphic computing. So they explored a number of CNN architectures, targeting image-based data that requires time-consuming feature extraction and classification. Given the broad interest of Berkeley Lab in the areas of structural biology, materials science, and cosmology, different scientists came together to select adequate problems that can be efficiently processed on the TrueNorth architecture.
In X-ray crystallography, a key step is to identify images with clear Bragg peaks, which are essentially bright spots created when light waves constructively interfere. Scientists typically keep images with Bragg peaks for further processing and discard those that don't have these features. Although an experienced scientist can easily spot these features, current software requires a lot of manual tuning to identify these features. Yang's team proposed to use a set of previously collected and labeled diffraction images to train a CNN to become a machine classifier. In addition to separating good images from bad ones, CNNs can also be used to segment the Bragg spots for subsequent analysis and indexing.
"Our detectors produce images at about 133 frames per second, but currently our software takes two seconds of CPU time to compute the answer. So one of our challenges is analyzing our data quickly," says Nicholas Sauter, a structural biologist in Berkeley Lab's Molecular Biophysics and Integrated Bioimaging Division. "We can buy expensive parallel computing systems to keep up with the processing demands, but our hope is that IBM TrueNorth may potentially provide us a way to save money and electrical power by putting a special chip on the back of the detector, which will have a CNN that can quickly do the job that those eight expensive computers sitting in a rack would otherwise do."
In cryo-electron microscopy, Yang and his teammates used simulated projection images to train a CNN to classify images into different orientation classes. For noise-free images, their CNN classifier successfully grouped images into as many as 84 distinct classes with over 90 percent success rate. The team also investigated the possibility of lowering the precision of the CNN by constraining both the input and CNN weights and found that reliable prediction can be made when the input and weights are constrained down to 3 or 4 bits. They are currently examining the reliability of this approach to noisy images.
"As the volume and complexity of science data increases, it will become important to optimize CNNs and explore cutting-edge architectures like TrueNorth," says Yang. "Currently, we are determining the CNN parameters — number of layers, size of the filters, and down sampling rate — with ad hoc estimates. In our future work, we would like to examine systematic approaches to optimizing these parameters."
For these LDRD projects, the researchers primarily used IBM's TrueNorth neuromorphic chip. In the future they hope to explore the viability of other neuromorphic computing architectures.
The work was funded through Berkeley Lab's Laboratory Directed Research and Development (LDRD) program designed to seed innovative science and new research directions. ALS and NERSC are DOE Office of Science User Facilities.
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