Submission

Abstract Submission Guidelines

Abstracts will be considered for Lightning Talks and Poster presentations. 

Submissions may include both published and unpublished work that tackle fundamental challenges for next-generation technologies, with a focus on novelty and the potential impact of these innovations on information processing, computing capabilities, and performance. We will prioritize submissions that present ambitious ideas and approaches. Selected submissions will be invited to deliver lightning talks and participate in poster presentations.

We will accept self-contained 1-page abstracts (an additional page may be used for references and figures) that will be reviewed by the technical program committee for novelty, quality, and clarity of the work.  

Please submit abstracts via Easy Chair: Click the link, then log in or create an account. Paste your 1- page abstract as plain text, or upload an attachment.  Indicate at least 1-2 cross-cutting themes as mentioned below. 

Please note 1 page for text and an additional page for figures and references (2 pages maximum).

Important Dates 

Abstract Deadline: November 2nd, 2026 (“Day of the Dead”/ Deadline)

Abstract Decision Notification: December 2nd, 2026

Event Dates: January 24th – 28th, 2027 at La Fonda in Santa Fe, New Mexico

Themes

Probabilistic Computing (Shimeng Yu, GT)
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, & 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, United Semiconductors, & Marco Fornari, DOE)
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.

Computation in Hostile Constrained Environments (Evan Kain, AFRL)

DoD has a need for high-performance, energy efficient computation in hostile environments, subject to ionizing or thermal radiation, with constrained energy budgets. Enabling low-power AI and neuromorphic architectures in hostile environments is  critical  in this area. This session explores novel solutions, including hardened electronics, novel algorithms, and system architectures that enable DoD missions.  

Cognitive Augmentation (Chou Hung, ARO)

This topic explores the design and deployment of neuromorphic computing systems tailored for wearable, head-mounted platforms — from AR/VR headsets to assistive cognitive interfaces. As demand grows for real-time, low-power, on-device intelligence, conventional von Neumann architectures struggle to meet the strict energy, latency, and form-factor constraints of head-worn hardware. Neuromorphic computing, inspired by the brain's event-driven, massively parallel processing, offers a promising path forward.