As computing systems become increasingly distributed, data-intensive, and interconnected, new approaches are needed to improve efficiency, adaptability, security, and scalability across modern computing infrastructure. Researchers at IEE are committed to developing intelligent computing architectures and adaptive infrastructure technologies that use AI-informed methods to optimize performance, resource management, and energy efficiency from cloud-scale systems to emerging hardware platforms.
This research theme focuses on the design of adaptive computing systems that dynamically respond to changing workloads, network conditions, and resource availability in real time. Faculty researchers are advancing distributed computing frameworks, cloud infrastructure, and resource-aware services capable of optimizing computation, storage, and communication across highly connected systems.Researchers are also exploring application-specific and hardware-software co-designed architectures that improve efficiency while enabling new computational capabilities. These efforts include intelligent monitoring and analysis systems, low-power processing architectures, high-throughput streaming analysis, and specialized computing platforms that leverage emerging devices and materials to enhance performance and reduce energy consumption.In parallel, the initiative addresses critical challenges in computing security, reliability, and system complexity. Faculty are developing architectures with verifiable security properties, adaptive control systems, and scalable distributed services that can maintain performance and resilience in the presence of fluctuating network conditions and evolving computational demands. These efforts aim to create the next generation of intelligent, energy-efficient computing infrastructure capable of supporting increasingly complex applications in AI, cloud computing, communications, and large-scale distributed systems.
Lead Faculty
Timothy Sherwood: Associate Professor, Computer Science
Timothy Sherwood's research is in the area of computer architecture, specifically in the development of novel high throughput hardware and software methods by which systems can be monitored and analyzed. Such techniques provide a powerful new way to inspect and control the digital world: they shed light on energy efficiency and performance anomalies, uncover software bugs, and help secure critical systems against attack.
Richard Wolski: Professor, Computer Science
Richard Wolski’s research interests include cloud computing, computational grid computing for performance, and parallel and distributed systems. He works with the Greenscale Center for Energy-Efficient Computing to restructure computations to be more energy aware through virtualization technology: a powerful tool with which to migrate and consolidate computations when used in conjunction with models and control of cooling technologies. Other recent endeavors include Eucalyptus: an open-source implementation of cloud computing that can emulate Amazon's EC2 on your own resources (commercialized as Eucalyptus Systems, Inc.); the Network Weather Service: a distributed system that periodically monitors and dynamically forecasts the performance that various network and computational resources can deliver over a given time interval; EveryWare: a toolkit for building high-performance globally distributed programs; and G-Commerce: market-based resource allocation strategies for the grid.
Grand Challenge
Multiple orders of magnitude improvement in Data Center energy efficiency.
Data centers are projected to see a 1000x increase in the volume of data and resulting computing and communications loads by the middle of this decade. To handle this dramatic increase in throughput while reducing data center energy consumption at all levels is a significant challenge that will require a multi-faceted approach. Institute researchers are working to improve the fundamental efficiency of algorithms, computing and systems architecture, communications and interconnect to achieve these aggressive goals.
Institute researchers are developing new computing and systems architecture solutions that reduce the energy use associated with cooling loads, inefficient server use, and wasteful computer processes. Our researchers are also developing new computing hardware and architectures that address AI processing problems in a dramatically more efficient way.
IEE’s world class photonics faculty have pioneered optical on-chip interconnects that are widely licensed and used in data centers around the world. These systems have dramatically improved the efficiency of data center interconnects and communications worldwide and our researchers new developments and emerging research promise even more progress in coming years.
Grand Challenge
1000X more efficient AI/machine learning training and processing.
Training AI/machine learning models, an activity that involves processing vast amounts of data, is an energy-intensive process. One recent estimate in the literature suggested that training a single typical AI/ML model creates a carbon dioxide (CO2) footprint of 626,000 pounds, or five times the lifetime emissions of the average American car. That same paper asserted that using a state-of-the-art language model for natural language processing equals the CO2 emissions of one human for 30 years. Both findings provide a jarring quantification of AI’s environmental impact.
IEE Machine Learning researchers have demonstrated significant early results in improving the energy efficiency of training AIs via an algorithmic and systems approach.
IEE’s researchers are also designing and fabricating hardware similar to the human brain, for solving some of the hardest AI and optimization problems. This effort employs mixed-signal neuromorphic circuits with integrated metal-oxide memristors. Such circuits enable very dense, fast, and energy-efficient implementation of the most common operations in bio-inspired optimization algorithms. The preliminary results from this group show that the proposed hardware implementation is estimated to be 70 times faster and 20,000 times more energy efficient compared to the most efficient conventional approach.
Grand Challenge
Develop the computational and communication energy efficiencies necessary to achieve a fully instrumented society using the Internet of Things (IoT).
The Internet of Things (IoT) embeds ordinary physical objects in our environment with sensing, control, communications, and computing capabilities. By making it easy and economical to collect, mine, and analyze information (extracting inferences and predictions) from any physical object and location, IoT has the potential for unparalleled societal impact beyond even that wrought by Internet search and e-commerce. IoT will facilitate data-driven automation and real time sensor analysis and tracking to enhance situational awareness and effective decision making by literally extending human perception and control of the physical world through digital infrastructure.
However, the power and energy requirements that must be met to instrument every “thing” and then to connect it to The Internet are staggering. For example, many accounts in the popular press predict 1 Trillion connected devices to be on the Internet within the next decade. If each device is 1 watt (the typical power consumption for a cell phone) that will require an additional 8700 TWh/year along with the concomitant ambient heat generation and carbon footprint. Further, the power infrastructure necessary to provide electrical power to remote areas is substantially more expensive than the instrumentation and actuation devices it will power.
Institute Professors Krintz and Wolski are developing the power optimized systems necessary to make the Internet of Things, and the societal benefits it will bring, a feasible reality. They have developed CSPOT – a portable, multi-scale programming infrastructure for cloud based IoT applications that is between 1 and 3 orders of magnitude more power efficient than current commercial IoT cloud systems. The CSPOT software corpus is also available as freely available open source, stimulating a community of IoT researchers and developers who are actively pursuing the ubiquity of IoT.As a proving ground for this research, Krintz and Wolski have developed the UCSB SmartFarm project that is developing the power-efficient IoT systems and analytics necessary to implement precision agriculture in rural locations where power and computational infrastructure are not available. SmartFarm provides Institute researchers with a rich set of test applications that require highly power-optimized and durable systems to enable new sustainable farming techniques.
Research Highlights
Energy Efficient Computation and Classification Professors Dmitri Strukov and Tim Sherwood - When extremely low-energy processing is required, the choice of data representation makes a tremendous difference and we have much to learn from nature in this regard. For example the brain seems to use some form of “time-based” representations — encodings where the temporal relationship between spike arrivals carries useful information. See for example, 2019 ASPLOS Best Paper Boosted Race Trees for Low Energy Classification.
UCSB Center for Responsible Machine Learning (CRML) Director William Wang - The Center for Responsible Machine Learning ties cutting-edge research in AI with important societal impacts. In their IEE related research, Professor Wang and colleagues in CRML are applying AI and Machine Learning to helping solve societal energy efficiency problems and are using an algorithmic and systems approach to making AI/ML training and inference dramatically more energy efficient.
Analytical Architectural Modelling Including Energy vs. Performance Tradeoffs Professor Tim Sherwood - CHARM (a language for Closed Form High-Level Architectural Modelling) and applying CHARM to design more energy efficient computing architectures.
Energy Efficient Programming for the Cloud Professors Chandra Krintz and Rich Wolski - Developed CSPOT – a portable, multi-scale programming infrastructure for cloud based IoT applications that is between 1 and 3 orders of magnitude more power efficient than current commercial IoT cloud systems. The CSPOT software corpus is also available as freely available open source, stimulating a community of IoT researchers and developers who are actively pursuing the ubiquity of IoT. Professor Wolski is also part of a team focused on The Zero-Carbon Cloud with colleagues at University of Chicago.
