Will Brain-Inspired Chips Make a Dent in Science’s Big Data Problems?
Two Berkeley Lab teams are running experiments on IBM’s TrueNorth chip to find out
July 5, 2017
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The average adult human brain is three pounds of mostly 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.
Our brains are also much better than computers at tasks like recognizing images and navigating unfamiliar spaces. Although the precise mechanism by which our brain performs these tasks is still unknown, we do know 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 way our minds do. 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 (DOE’s) Lawrence Berkeley National Laboratory (Berkeley Lab) 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.
“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.” - Paolo Calafiura, CRD Scientist.
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.
He adds: “For one reason or another—be it I/O (input/output), CPU (computer processing unit) or memory—every computing platform that we’ve come across so far hasn’t been able to scale to meet our data processing needs. 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.”
Why Neuromorphic Computing?
In the traditional von Neumann design, computers are comprised primarily of two components: a CPU that handles data, and random access memory (RAM) 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 colocated. 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.”
Translating Science Methods: From von Neumann to Neuromorphic
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. Here is a more detailed look at these two projects.
Particle Physics and Brain Machine Interfaces
Co-leads: Kristofer Bouchard and Paolo Calafiura
In particle physics experiments, researchers smash beams of protons at the center of detectors and measure the energy and momentum of escaping particles. By tracking the trajectory of escaping material with algorithms called Kalman filters, physicists can infer the existence of massive particles that were created, or decayed, right after the collision.
Kalman filters are essentially optimal estimators. They can infer structures of interest, relatively accurately, from a series of measurements taken over time in difficult environments that produce data with statistical noise and other inaccuracies. Because these algorithms are recursive, new measurements can be processed in real time, making them convenient for online processing. In addition to particle physics, Kalman filters are also widely used for navigation, signal processing and even modeling the central nervous system’s control of movement.
Currently, Bouchard and 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.
“Brain-machine interfaces (BMIs) for restoring lost behavioral functions entail recording brain signals and transforming them for a particular task. The computations required for a BMI need to occur in real time, as delays can cause instabilities in the system,” says Bouchard. “Today, the majority of state-of-the-art BMIs utilizes some variation of the Kalman filter for transforming observed brain signals into a prediction of intended behavior.”
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 collected by Dr. Edward Chang at the University of California, San Francisco. This consists of neural recordings from 100-256 electrodes with signal rates of ~400 Hz, well within the constraints of a single TrueNorth system. 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.
Image Analysis and Pattern Recognition
Co-leads: Chao Yang, Nick Sauter and Dani Ushizima
Convolutional neural networks are extremely useful for image recognition and classification. In fact, companies like Google and Facebook are using 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. CNNs learn from these tags, so the next time someone tries to tag a face in an uploaded image the system can offer suggestions based on what it’s learned.
Because CNN designs evolved from early research of the brain’s visual cortex and how neurons propagate information through complex cell organizations, Yang and his colleagues 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 biology and materials science, X-ray crystallography is a popular technique for determining the three-dimensional atomic structure of salts, minerals, organic compounds, and proteins. When researchers tap the crystalline atoms or molecules with an X-ray beam, light is scattered in many directions. By measuring the angles and intensities of these diffracted beams, scientists can create a 3D picture of the density of electrons inside the crystals.
One of the key steps in X-ray crystallography 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.”
Cryo-Electron Microscopy (CryoEM)
To determine the 3D structures of molecules without crystalizing them first, researchers use a method called cryo-electron microscopy (cryoEM), which involves freezing a large number of randomly oriented and purified samples and photographing them with electrons instead of light. The 2D projected views of randomly oriented but identical particles are then assembled to generate a near-atomic resolution 3D structure of the molecule.
Because cryoEM images tend to have very low signal-to-noise ratio—meaning it is relatively hard to spot the desired feature from the background—one of the key steps in the analysis process is to group images with the similar views into the same class. Averaging images within the same class boosts the signal-to-noise ratio.
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.
Grazing Incidence Small Angle X-ray Scattering (GISAXS)
Grazing incidence small angle X-ray scattering (GISAXS) is an imaging technique used for studying thin films that play a vital role as building blocks for the next generation of renewable energy technology. One of the challenges in GISAXS imaging is to accurately infer the crystal structure of a sample from its two-dimensional diffraction pattern.
In collaboration with Advanced Light Source (ALS) Scientist Alex Hexemer, Ushizima used categorization algorithms to label large collections of computer simulated-images, each containing a variety of crystal structures. They used this dataset to train a deep CNN to classify these images by their structures. When they tested the performance of their classifier on multiple datasets, they achieved about 83 to 92 percent accuracy depending on the number of crystal lattices of each test case. Preliminary classification results using real images point out that models trained on massive simulations, including realistic background noise levels, have the potential to enable categorization of experimentally obtained data.
“We believe that these initial results are really encouraging, and an indication that we should continue to study the use of CNNs for GISAXS and other synchrotron based scientific experiments,” says Ushizima.
To find Type Ia supernovas and other transient events in the night sky, astronomers rely on sky surveys that image the same patches of sky every night for months and years. Astronomers warp and average these some of images together to create a template of a particular patch of sky. When a new observation comes in, they will compare it to the template and subtract the known objects to uncover new events like supernova. Because images of the night sky have to be warped to correct for optical effects or artifacts—caused by defect sensors, cosmic ray hits and foreground objects—the subtractions are not always perfect. In fact, 93 percent of potential candidates identified by the subtraction pipeline are artifacts.
To sift out the false from real candidates post-subtraction, Thorsten Kurth, an HPC Consultant at the National Energy Research Scientific Computing Center (NERSC) created a two-layer CNN and applied a method that involved 80 percent training, 10 percent validation and 10 percent testing to evaluate the performance of their algorithm on TrueNorth. To test the robustness of his algorithm, he also included images of the night sky in varying orientations in their training dataset. Ultimately, they achieved about 95 percent classification accuracy.
“Increasing the network with more layers does not mean to improve performance,” says Ushizima. “The next step involves trying our approach on a different dataset, which contains images with low signal-to-noise ratio, images with defects, as well as noise and defect pixel maps. With this dataset, the neural network can learn correlations between all those characteristics and thus hopefully deliver a better performance.”
Microtomography (MicroCT) is an imaging method that is very similar to what hospitals use when they do CT or CATscans on a patient, but it images on a much smaller scale. It actually allows researchers to image the internal structure of objects at very fine scales and in a non-destructive way. This means that no sample preparation needs to occur—no staining, no thin slicing—and a single scan can capture the sample’s complete internal structure in 3D and at high resolution.
Using microCT, scientists can test the robustness of materials that may one day be used in batteries, automobiles, airplanes, etc. by searching for microscopic deformations in its internal structure. But sometimes finding these fissures can be a lot like searching for a needle in a haystack. So Ushizima and Yang teamed up with the ALS’s Dula Parkinson to develop algorithms to extract these features from raw microCT images.
“Computer vision algorithms have allowed us to construct labeled data banks to support supervised learning algorithms, like CNNs. One particular tool that we created allows the researcher to segment and label image samples with high accuracy by providing an intuitive user interface and mechanisms to curate data,” says Ushizima.
Although these tools were developed specifically to extract features from microCT images, she notes that it is applicable to other science areas as well.
“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 because it was the first neuromorphic chip they had access to. In the future they hope to explore the viability of other neuromorphic computing architectures.
In addition to Yang, Ushizima, Sauter, Hexemer and Kurst, the other Berkeley Lab collaborators on the Image Analysis and Pattern Recognition LDRD include Karen Davies (MBIB), Xiaoye Li (CRD), Peter Nugent (CRD, NERSC), Dilworth Parkinson (ALS), Nicholas Sauter (MBIB) and Singanallur Venkatakrishnan (ORNL). In addition to Bouchard and Calafiura, the other collaborators on the Particle Physics and Brain Machine Interfaces LDRD include David Donofrio (CRD), Maurice Garcia-Sciveres and Rebecca Carney (Physics), David Clarke and Jesse Livezey (UCB, CRD).
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.
About Computing Sciences at Berkeley Lab
The Computing Sciences Area at Lawrence Berkeley National Laboratory provides the computing and networking resources and expertise critical to advancing Department of Energy Office of Science research missions: developing new energy sources, improving energy efficiency, developing new materials, and increasing our understanding of ourselves, our world, and our universe.
Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 13 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science.
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