The remarkable progress in artificial intelligence (AI) has ushered in a new era characterized by models with billions of parameters, enabling extraordinary capabilities across diverse domains. However, these achievements come at a significant cost in terms of memory and energy consumption. The growing demand for computational resources raises grand challenges for the sustainable development of energy-efficient AI systems. This paper delves into the paradigm of memory-based computing as a promising avenue to address these challenges. By capitalizing on the inherent characteristics of memory and its efficient utilization, memory-based computing offers a novel approach to enhance AI performance while reducing the associated energy costs. Our paper systematically analyzes the multifaceted aspects of this paradigm, highlighting its potential benefits and outlining the challenges it poses. Through an exploration of various methodologies, architectures, and algorithms, we elucidate the intricate interplay between memory utilization, computational efficiency, and AI model complexity. Furthermore, we review the evolving area of hardware and software solutions for memory-based computing, underscoring their implications for achieving energy-efficient AI systems. As AI continues its rapid evolution, identifying the key challenges and insights presented in this paper serve as a foundational guide for researchers striving to navigate the complex field of memory-based computing and its pivotal role in shaping the future of energy-efficient AI.
more »
« less
This content will become publicly available on July 9, 2025
Geographical Server Relocation: Opportunities and Challenges
The enormous growth of AI computing has led to a surging demand for electricity. To stem the resulting energy cost and environmental impact, this paper explores opportunities enabled by the increasing hardware heterogeneity and introduces the concept of Geographical Server Relocation (GSR). Specifically, GSR physically balances the available AI servers across geographically distributed data centers subject to AI computing demand and power capacity constraints in each location. The key idea of GSR is to relocate older and less energy-efficient servers to regions with more renewables, better water efficiencies and/or lower electricity prices. Our case study demonstrates that, even with modest flexibility of relocation, GSR can substantially reduce the total operational environmental footprints and operation costs of AI computing. We conclude this paper by discussing major challenges of GSR, including service migration, software management, and algorithms.
more »
« less
- PAR ID:
- 10544904
- Publisher / Repository:
- 2024 HotCarbon Workshop on Sustainable Computer Systems
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Much of the world’s data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.
-
more » « less
Abstract Electricity and water systems are inextricably linked through water demands for energy generation, and through energy demands for using, moving, and treating water and wastewater. Climate change may stress these interdependencies, together referred to as the energy-water nexus, by reducing water availability for hydropower generation and by increasing irrigation and electricity demand for groundwater pumping, among other feedbacks. Further, many climate adaptation measures to augment water supplies—such as water recycling and desalination—are energy-intensive. However, water and electricity system climate vulnerabilities and adaptations are often studied in isolation, without considering how multiple interactive risks may compound. This paper reviews the fragmented literature and develops a generalized framework for understanding these implications of climate change on the energy-water nexus. We apply this framework in a case study to quantify end-century direct climate impacts on California’s water and electricity resources and estimate the magnitude of the indirect cross-sectoral feedback of electricity demand from various water adaptation strategies. Our results show that increased space cooling demand and decreased hydropower generation are the most significant direct climate change impacts on California’s electricity sector by end-century. In California’s water sector, climate change impacts directly on surface water availability exceed demand changes, but have considerable uncertainty, both in direction and magnitude. Additionally, we find that the energy demands of water sector climate adaptations could significantly affect California’s future electricity system needs. If the worst-case water shortage occurs under climate change, water-conserving adaptation measures can provide large energy savings co-benefits, but other energy-intensive water adaptations may double the direct impacts of climate change on the state’s electricity resource requirement. These results highlight the value of coordinated adaptation planning between the energy and water sectors to achieve mutually beneficial solutions for climate resilience.
-
more » « less
Abstract Although a recent report by the U.S. Energy Information Administration indicates that CO2emissions from power generation have now fallen below emissions from the transportation sector, electricity use remains a significant part of human's environmental footprint since fossil fuels are the major energy resource for electricity generation worldwide. This explains the continual effort by policy makers to develop a remedy to mitigate carbon emissions. It is then critically important to properly account for the emissions associated with electricity generation, delivery, and consumption. In particular, it is critical to be able to allocate emissions to the relevant electricity consumers creating the demand that results in a polluting emission. Therefore, in this paper a
virtual carbon emission tracing method in power systems is thoroughly presented to facilitate allotting carbon obligation. The method is developed based on the proportional sharing principle that has been used in power system operation. The proposed methodology is implemented on the IEEE 5‐bus and 9‐bus systems, two widely used and recognized power grid benchmarks. -
Space cooling constitutes >10% of worldwide electricity consumption and is anticipated to rise swiftly due to intensified heatwaves under emerging climate change. The escalating electricity demand for cooling services will challenge already stressed power grids, especially during peak times of demand. To address this, the adoption of demand response to adjust building energy use on the end-user side becomes increasingly important to adapt future smart buildings with rapidly growing renewable energy sources. However, existing demand response strategies predominantly explore sensible cooling energy as flexible building load while neglecting latent cooling energy, which constitutes significant portions of total energy use of buildings in humid climates. Hence, this paper aims to evaluate the demand response potential by adjusting latent cooling energy through ventilation control for typical medium commercial office buildings in four representative cities across different humid climate zones, i.e., Miami, Huston, Atlanta, and New York in the United States (US). As the first step, the sensible heat ratio, defined as sensible cooling load to total building load (involving both sensible and latent load), in different humid climates are calculated. Subsequently, the strategy to adjust building latent load through ventilation control (LLVC) is explored and implemented for demand response considering the balance of energy shifting, indoor air quality, and energy cost. Results reveal that adjusting building ventilation is capable of achieving 30%–40% Heating, Ventilation, and Air-conditioning (HVAC) cooling demand flexibility during HVAC operation while among this, the latent cooling energy contributes 56% ~ 66.4% to the overall demand flexibility. This work provides a feasible way to improve electricity grid flexibility in humid climates, emphasizing the significant role of adjusting latent cooling energy in building demand response.more » « less
