Three projects that take innovative approaches to solving critical energy-efficiency challenges have been awarded seed funding from UC Santa Barbara’s Institute for Energy Efficiency (IEE), an interdisciplinary research institute committed to improving energy efficiency. The selected projects align with at least one of the institute’s three key interdisciplinary thrusts: smart societal infrastructure, computing and communications, and the food-energy-water nexus. Each proposal will receive up to $50,000 in critical seed funding, which is intended to produce preliminary results that the scientists can use to apply for major external funding to expand their research.
“Supporting projects in the early stages of development is an essential step to the creation and delivery of high-impact solutions to improve efficiency,” said IEE director, John Bowers, a distinguished professor of electrical and computer engineering (ECE) and materials. “Each project attacks a grand challenge with a leading-edge approach that has tremendous potential. They also foster new research collaborations and leverage IEE’s legacy of scientific discovery.”
The awards bring six new faculty members from the departments of mechanical engineering, computer science, ECE, and physics to the IEE.
“The review committee was thrilled with these projects because they proposed innovative paths to discovery and enrolled new junior and senior faculty in the institute,” said Mark Abel, associate director and executive advisor of IEE. “We also saw how seed funding could really help these faculty move their new efforts forward, and how the research, if successful, could lead to additional funding opportunities.”
The selected projects are aimed at developing novel technologies, ranging from new computer hardware to a probabilistic computer, to an energy harvester that powers portable and remote smart devices.
Miniature Energy Harvesters
The Internet of Things (IoT) is a giant network of connected devices, all of which collect and share data about the way they are used and their environment. The “smart” devices that make up the IoT have sensors to collect the data that make it possible to light a room or heat a home with greater energy efficiency. The sensors require power to operate, and batteries are often used to power portable devices. However, batteries’ weight, limited energy storage capacity, and short lifespans have sparked a push for zero-power technologies with energy autonomy, specifically ones capable of harvesting and storing energy from their surrounding environments. To date, most of the work on portable power applications in the field has involved optimizing existing technologies, and relatively few fundamentally new ideas have been explored. But now, an interdisciplinary team of UCSB researchers, comprising mechanical engineering professors Sumita Pennathur and Carl Meinhart, and physics associate professor David Weld, has received seed funding to begin developing a device that generates electrostatic energy from mechanical power sources, such as vibrations.
“We proposed a novel device that will harvest energy through pressures and vibrations from the surrounding environment, thereby eliminating the power requirements of batteries in portable and/or remote IoT devices,” said Pennathur, an elected fellow of the American Institute for Medical and Biological Engineers (AIMBE) and recipient of the prestigious Presidential Early Career Award in Science and Engineering (PECASE).
Their novel energy harvester is called LIMMPET, which stands for Liquid-Metal Microfluidic Portable Energy Transducer. The device uses a multiphase system composed of droplets of liquid mercury surrounded by insulating oil to exploit, through a clever microfluidic geometry, the electrostatic induction between adjacent metal droplets. The geometry consists of a system of external electrodes that allow the team to collect and amplify the induced charge.
Prior to applying for the grant, Pennathur and Weld designed a prototype, and their initial results showed that the power produced was less than the theoretical maximum. By adding Meinhart, who specializes in designing simulation techniques to understand the physics of transport phenomena at the micro- and nanoscales, Pennathur believes they can make the necessary adjustments to realize the full promise of this new technology.
“Professor Weld came up with the idea to miniaturize a Wimshurst machine, which harvests mechanical energy and transforms it into electrical power that can be used to power IoTs,” explains Pennathur. “My lab provided the microfluidic expertise needed to go from concept to implementation. Professor Meinhart is an expert in COMSOL modeling, and his contribution will help tease out the physical principles behind the LIMMPET and allow us to optimize the device. Our complementary skills and expertise make this a true and ideal collaboration.”
Through the seedling project, they hope to develop and validate a model to guide changes to the channel geometry and understand all of the mechanisms and device configurations that yield maximum power efficiency. The results could not only show the feasibility of a novel pressure-powered energy harvester, but also provide enough initial data to pursue larger sources of funding to expand their program
“If our project is successful, LIMMPET can be a huge step in miniature energy harvester technology,” said Pennathur. “That means we can have remote IoTs all over the place, without the need for batteries or grid power. What is even more exciting is that we propose a completely novel technology to generate ‘clean’ energy.”
Computer processors — digital circuits that perform operations on external data sources — have become more complex over the years, as their performance and efficiency have improved dramatically. One way that computer architects have increased their efficiency is by exploiting the predictability or “locality” of applications, designing them to save information, or learn as they run. Therefore, the longer an application runs, the more efficiently the processor can execute it. On the flip side, short-running applications are often significantly less efficient because processors cannot learn as much and are forced to start from scratch each time they are activated. Jonathan Balkind, an assistant professor of computer science, has received seed funding to improve the efficiency of short-term applications by focusing on building mechanisms, which he calls microarchitectural checkpoints, into the processor.
“The checkpoints enable us to save what processors have learned for later in order to make these applications more efficient,” said Balkind, who joined UCSB in July 2020, after completing his PhD in computer science at Princeton. “The checkpoints would include what data is being frequently used or what paths of execution the program is likely to take. They would save that information when the program is shut down, so that when it is started up again, that checkpoint would load and, we hope, run the application far more efficiently by exploiting what was learned the last time it ran.”
The checkpoints could improve the efficiency of two major computing domains that rely on short-term applications: intermittent computing and Function-as-a-Service (FaaS). Intermittent computing systems perform as much computation as they can out of a limited energy supply, such as a solar-charged battery. These devices typically lose power while performing the work. Balkind proposes adding a new microarchitectural checkpointing mechanism into the hardware that can be customized to the particular application that will be run.
“Since the processor may be turning on and off unpredictably, it’s difficult to build systems that can continue to execute useful applications and know if the data has been safely stored or whether it will work once the power returns,” explained Balkind. “There is a lot of research for these systems that we are interested in applying microarchitectural checkpointing to in order to see what benefits it can give us.”
FaaS is a type of serverless computing and a way to deploy applications in the cloud by loading functions and launching on demand. FaaS works well for stateless services, which rely only on information relayed with each request and do not rely on information from previous operations. However, the stateless and short-lived functions are problematic because they lose locality or reference, forcing the application to reload and process the information each time. Balkind’s previous research verified that FaaS applications were short running and could not predict or reference past information.
“We propose adding mechanisms to the processor to save and restore the state of these locality-preserving structures, in order to start functions in a well-trained state, ready to operate with high efficiency,” said Balkind. “Realizing such capability will make FaaS a more predictable and efficient application that will be more attractive to developers.”
Balkind says the project could lead to the design of machines designed specifically for running short-lived applications like FaaS, as well as the rapid growth of energy-efficient IoT applications made possible by intermittent computing.
Forty years ago, while speaking to computer scientists at a conference, Nobel Laurate Richard Feynman introduced the idea of using quantum computers to solve quantum mechanical problems. He also reasoned that probabilistic computers would be best suited to solving inherently probabilistic tasks, such as decision making in the presence of uncertainty.
“We could imagine and be perfectly happy…with a probabilistic simulator of a probabilistic nature, in which the machine doesn’t exactly do what nature does,” Feynman said. “But if you repeated a particular type of experiment a sufficient number of times to determine nature’s probability, then… you’d get the corresponding probability with the corresponding accuracy.”
Feynman’s words have inspired scientists ever since, sparking a furious push toward demonstrating the advantages of quantum computers, which are powered by qubits. Unlike the bits that power classical computers and can exist in a state of 0 or 1, a qubit is a two-state system that can be in more than one state at a time. The phenomenon, known as superposition, enables quantum computers to perform computational functions exponentially faster than classic computers can. However, qubits must be kept at extremely low temperatures, requiring significant amounts of energy.
While scientists continue to address the challenges of quantum computers, two electrical and computer engineering faculty members at UCSB, Kerem Camsari and Luke Theogarajan, have received seed funding to design the other type of computer that Feynman envisioned, one that could act more like nature.
“The goal of our project is to build a probabilistic computer to solve computationally hard problems of artificial intelligence (AI) and machine learning (ML),” said Camsari, an assistant professor who joined the UCSB faculty in July 2020.
“A key step in AI and ML is making decisions based on incomplete data. The best approach for this is to output a probability for each possible answer,” said Theogarajan, who is the vice-chair of the ECE Department and has extensive experience in building heterogenous circuits and architecture. “Current systems are not suited for energy-efficient execution of these problems. Probabilistic machines really shine through in these tasks. Our goal is to take this vision and translate it into energy-efficient custom hardware.”
The seedling project is an extension of the research Camsari conducted while completing his PhD and a postdoctoral position at Purdue University. His team worked with other collaborators to build a small-scale probabilistic computer, comprising probabilistic bits, or p-bits, which interact with others in the same system. Unlike the qubits of quantum computers, which occur in two states (superposition), p-bits fluctuate between positions. The prototype, implemented with nanodevices, demonstrated two encouraging results: it was capable of solving the same optimization problems often targeted for quantum computers, and provided a ten-fold reduction in the energy it required compared to a classical computer.
“The advantage of p-bits compared to qubits is that they operate at room temperature. This means there are many possible implementations of p-bits,” explains Camsari. “A key realization for us was that even if p-bits are not direct substitutes for qubits, many problems of the emerging AI and ML algorithms can benefit from the development of p-computers.”
Theogarajan and Camsari received funding from the IEE to build a medium-sized probabilistic computer, using complementary metal-oxide-semiconductor (CMOS) technology, an area in which Theogarajan specializes. They say that scaling up the prototype to thousands of CMOS p-bits will provide the framework to realize the true power of p-computing and identify the key benchmarks and problems targeted by p-computers. The pair has laid out a three-step plan of attack in which they hope to: develop algorithms specific for p-computers; design energy-efficient architectures and p-bits; and build and test p-computers and evaluate them against classical computers.
“Being new at the College of Engineering makes this award especially significant and meaningful to me,” said Camsari. “Luke is a leading analog/digital CMOS designer with an eye for new applications of p-computing, especially in biology and neuroscience. I feel fortunate to be able to collaborate with him to take p-computing in a new direction.”
“Seed funding from IEE gives me the opportunity to collaborate in a meaningful way with a new faculty member in the department,” added Theogarajan. “It also helps us explore ideas and get the insight and results we need to seek more funding to expand our project.”
The success of this program would fill an important gap for probabilistic computing by providing a significantly scaled-up version of experimental prototypes. The insights gained could pave the way for efficient p-computers capable of revolutionizing the fields of randomized numerical linear algebra, ML, and probabilistic inference in AI.
“The potential impact of p-computers involves optimization, drug discovery, protein folding, and other challenges that people have been hoping near-term quantum computers could solve,” said Camsari.
Seed funding is supported by gifts from private donors, such as John MacFarlane, a member of the IEE Directors Council. Support from an anonymous donor allowed the IEE to award an additional grant this year. Those interested in providing seed funding should contact Mark Abel at email@example.com.