Main Theme: The Next Decade of Co-Design Innovation

The Energy Consequences of Information (ECI) Workshop 2027 invites researchers to contribute to advancing the fundamental science and emerging technologies shaping the future of energy-efficient computing and information processing. As demands from artificial intelligence, scientific computing, communications, sensing, and edge systems continue to escalate, understanding the energy costs of computation—and uncovering innovative strategies to reduce them—has become a critical, cross-disciplinary challenge.

ECI 2027 will convene a diverse community of experts from neuroscience, materials science, device physics, circuits, computer architecture, algorithms, photonics, quantum computing, bioengineering, and high-performance computing. The workshop will spotlight bold, interdisciplinary ideas that bridge fundamental limits with practical systems, unveiling new pathways to enhanced performance, efficiency, adaptability, and scalability.

Submission Guidelines

We welcome submissions on a wide range of topics, including probabilistic computing, learning algorithms, spiking neural networks, bioengineered and neuromorphic sensing and computation, photonics, novel and ferroelectric devices, neuroscience-inspired architectures, quantum computing, full-stack co-design, scientific computing, and studies exploring the thermodynamic, physical, and architectural limits of computation. Contributions may encompass theoretical advances, experimental results, modeling and simulation, hardware demonstrations, benchmarking, and application-driven system perspectives.

ECI 2027 especially encourages work that transcends traditional disciplinary boundaries and uncovers new performance-per-watt opportunities through innovative co-design of devices, algorithms, and architectures for next-generation computing.

Abstracts will be considered for both Lightning Talks and Poster presentations.

We invite submissions of both published and unpublished work that address fundamental challenges in next-generation technologies, emphasizing novelty and the potential impact on information processing, computing capabilities, and performance. Priority will be given to submissions presenting ambitious ideas and innovative approaches. Selected abstracts will be invited to deliver Lightning Talks and participate in Poster sessions.

Please submit a self-contained abstract of up to 2 pages (1 page for text plus 1 additional page for figures and references). Abstracts will be reviewed by the technical program committee based on novelty, quality, and clarity.

To submit, please use EasyChair: You may paste your 1-page text abstract as plain text or upload it as an attachment. Be sure to indicate at least 1–2 relevant cross-cutting themes from the list below.

ThemesThemes

Includes a short description and the session chair/(s) for that theme.

Probabilistic Computing (Shimeng Yu, Georgia Tech & Nikhil Shukla, UVA)
Explores stochastic and physics-based computing paradigms, including Ising machines, probabilistic inference, sampling, and optimization. Topics include algorithms, circuits, devices, co-design, benchmarking, scalability, and applications in AI and scientific computing.

Analog Learning Algorithms (Frank Barrows, LANL)
Focuses on analog and mixed-signal approaches that use device physics not only to compute, but also to train, including the principles, mechanisms, and benchmarks needed to establish distinctive analog learning paradigms and advantages. Contributions spanning algorithms, hardware implementations, and system evaluation are welcome.

Accelerated Learning in Spiking Neural Networks (Xiaoxuan Yang, UVA)
Addresses efficient training and deployment of spiking neural networks, including local, online, and global learning strategies. Emphasis is placed on connecting neuron models and training methods to hardware-relevant efficiency gains.

Bio-engineered Sensing and Computation (Grace Hwang & Joseph Monaco, NIH BRAIN Initiative®)
Covers bio-inspired/neuromorphic and bio-integrated systems for sensing, neuromodulation, brain-computer interfaces, and biocomputation. Relevant topics include adaptive materials, convergence of living systems with silicon, and novel approaches to robust, continual learning, and low-power biological interfacing.

Photonics for Computing (Midya Parto, UCF)
Examines photonic approaches to information processing across classical, neuromorphic, and quantum settings. Topics include materials, fabrication, modulators, light sources, photonic devices, and their role in energy-efficient and high-performance computing systems.

Novel and Emerging Devices / Ferroelectrics (Yuping Zeng, UD)
Highlights emerging low-power devices for computing, including ferroelectric, memristive, spintronic, and in-memory technologies. Contributions may address simulation, reliability, prototyping, and design enablement for new device classes.

From Neural Data to Neuromorphic Principles (William Chapman, SNL) Focuses on how neuroscience data can be transformed, through theory and modeling, into generalizable principles for neuromorphic computing. Contributions should clarify the abstraction bottleneck between biological measurements and engineered systems, identifying what must be preserved, abstracted, or discarded to guide neuromorphic design.

The Next Frontier for Energy and Computing Performance (Margaret Kim, NSF, n& Robinson Pino, United Semiconductors)
Addresses transformative directions for improving performance per watt across HPC, edge computing, wireless systems, and AI infrastructure. Contributions may explore physical limits, unconventional technologies, and AI-assisted acceleration of new computing paradigms.

Quantum Computing (Robinson Pino & Marco Fornari)
Examines the energy and performance implications of quantum computing, including complexity-theoretic considerations, space-time tradeoffs, and the practical consequences of quantum architectures for future computing systems.

Architectures, and Co-Design (Suma Cardwell, SNL)
A cross-cutting theme addressing thermodynamic and physical limits, architecture, scientific computing, AI-enabled automation, benchmarking, and full-stack co-design. Contributions that connect materials, devices, circuits, algorithms, and applications are especially encouraged.

Cognitive Augmentation (Chou Hung, ARO)
Advancing neuromorphic computing for head-mounted devices.

CommitteesCommittees

Steering Technical Program Committee

• Dr. Frank Barrows (LANL)
• Dr. William Chapman (SNL)
• Dr. Shimeng Yu (Georgia Tech)
• Dr. Xiaoxuan Yang (UVA)
• Dr. Midya Parto (UCF)
• Dr. Yuping Zeng (UD)
• Dr. Joseph Monaco (NIH)
• Dr. Grace Hwang (NIH)
• Dr. Margaret Kim (NSF)
• Dr. Robinson Pino (United Semiconductors)
• Dr. Chou Hung (ARO)
• Dr. Nikhil Shukla (UVA)
• Dr. Suma George Cardwell (SNL)

Organizing Committee