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Agenda is tentative and subject to change. All times are in MDT.
Monday, February 2
5:00 - 7:00pm Welcome Reception
Tuesday, February 3
8:00 - 8:15 Welcome and Workshop Introduction – Art Edwards, AFRL
8:15 - 8:45 Workshop context setting – Suma Cardwell, SNL
Topic A: Where is AI today and where do we need to go?
8:45 - 9:05: PLENARY TALK
9:05 - 9:25 KEYNOTE
9:25 - 9:35 Continual Learning – Dhireesha Kudithipudi, UT San Antonio
9:35 - 9:45 Efficient inference and learning in recurrent neural circuits – Zeyu Yun, UC Berkeley
9:45 - 10:05 Panel Discussion
Topic B: AI at the edge/ training
10:05 - 10:25 KEYNOTE
10:25 - 10:35 Understanding and Optimizing Quantization-enabled LLM Serving Systems: A Performance, Energy, and Quality Perspective – Yi Ding, Purdue University
10:35 - 10:45 Spiking Neural Networks Integration in Heterogeneous Architectures with MoSAIC – Farzad Fatollahi-Fard, LBL
10:45 - 10:55 Enable Native Full-Integer Spiking Neural Network Training with Algorithm and Hardware Co-Design – Xiaoxuan Yang, University of Virginia
10:55 - 11:05 Neuromorphic Signal Processing at the Edge: Energy-Efficient SNNs for Audio Processing – Kendric Hood, Kent State University
11:05 - 11:25 Panel Discussion
11:25 - 11:35 *Break before Working Lunch Keynote*
11:35 - 12:35 LUNCH KEYNOTE - Jim Lyke, AFRL/AFOSR
12:35 - 12:50 *Break*
Topic C: Frameworks for fundamental energetic limitations
12:50 - 1:10 KEYNOTE
1:10 - 1:20 Development of a Practical Adiabatic Reversible CMOS (ARC) Technology for High-Performance Computing – Mike Frank, VAIRE
1:20 - 1:30
1:30 - 1:40 Reliable AI in Unreliable Energy-Constrained Distributed Environments – George Michelogiannakis, LBL
1:40 - 1:50 Energy-Efficient Solar Forecasting with a Neuromorphic-Bayesian State Estimation Approach, Kumar Anurag, University of New Mexico
1:50 - 2:10 Panel Discussion
Topic D: Next-Generation Technologies
2:10 - 2:30 KEYNOTE
2:30 - 2:40
2:40 - 2:50
2:50 - 3:00 Casper: Cascading Hypernetworks- for Scalable Continual Learning – Tej Pandit, UT San Antonio
3:00 - 3:10 Neuro-Symbolic Architectures for Scalable and Energy-Efficient Computation: Opportunities and Open Challenges – Shay Snyder/Maryam Parsa, George Manson University
3:10 - 3:30 Panel Discussion
Topic E: Next-Generation Technologies -Session 2
3:30 - 3:50 KEYNOTE: Edge of Chaos and Entropy Production Minimization as Keys to Neuromorphic Computing – Stan Williams, Texas A&M University
3:50 - 4:00 Energy and Information in Open Thermodynamic Systems – Narayan Srinivasa, Intel
4:00 - 4:10 Advanced packaging for low power, weight and form factor bio-inspired sensing platform – Anastasiia Butko, LBL
4:10 - 4:20 Reshaping LLM Edge Architecture by Emerging Memory Technologies – Ramtin Zand, University of South Carolina
4:20 - 4:30 Neuromorphic Energy-Efficient Multi-Layer Learning with Compartmental Spiking Neurons – Ashish Gautam, ORNL
4:30 - 4:50 Panel Discussion
4:50 - 5:00 Day 1 closeout (Observations and Day 2 Outlook)
5:00 - 5:30 Poster Summary Blitz
5:30 - 7:30 Evening Session: RUMP Session
Wednesday, February 4
8:30 - 8:45 Day 2 Welcome
Topic F: Neuromorphic Primitives and Circuit Design
8:45 - 9:05 KEYNOTE: – Jennifer Hasler, Georgia Tech
9:05 - 9:15 Time-Domain Neuromorphic Computing Circuits – Cory Merkel, Rochester Institute of Technology
9:15 - 9:25 Biological Plausibility and Energy Efficiency in Artificially Intelligent Learning Hardware – Joseph Friedman, UT Dallas
9:25 - 9:35 Efficient Log-Domain Circuit Realization of Biologically-Plausible Neurons – Abdulkarim Alorf, Duke University
9:35 - 9:45 Biologically-Inspired Multiscale Neuromorphic Architecture – Christian O’Reilly, University of South Carolina
9:45 - 10:05 Panel Discussion
Topic G: Novel Use-Cases for Emerging Devices
10:05 - 10:25 KEYNOTE: Integration of Efficient Sensing, Encoding, and Learning via Memristive Dynamics – Joshua Yang, University of Southern California
10:25 - 10:35 Memristor-Based Spiking Neural Network Accelerator for Neuromorphic Computing – Chenyun Pan, UT Arlington
10:35 - 10:45 Energy-efficient and Scalable Inferencing with Hardware Innovation – Beyond-CMOS Technologies – Guojing Cong, ORNL
10:45 - 10:55 Energy-Efficient Temporal Processing with Artificial Spin Ice Based Neuromorphic Nonlinear Vector Autoregression Machine – Ishan Thakkar, University of Kentucky
10:55 - 11:05 Hybrid Opto-electronic Integrated Encoders for Ultralow Power Data Compression – Nicholas Nobile, SNL
11:05 - 11:25 Panel Discussion
11:25 - 11:35 *Break before Working Lunch Keynote*
11:35-12:35 LUNCH KEYNOTE
12:35 - 12:50 *Break*
Topic H: Beyond CMOS/ Emerging Devices and Technologies
12:50 - 1:10 KEYNOTE: Emerging devices for Neuromorphic Computing – Yuping Zeng, Univ of Delaware
1:10 - 1:20 Modeling Ferroelectric and Antiferroelectric Capacitors with Physics-Informed LSTMs – Nicholas Ramos/Hai Li, Duke University
1:20 - 1:30 Van der Waals ferroelectric tunnel junctions for next-generation logic-in-memory hardware – Cheng Gong, University of Maryland
1:30 - 1:40 First-Principles Investigation of the Resistive Switching Energetics in Monolayer MoS2: Insights into Metal Diffusion and Adsorption – Jameela Fatheema, UT Austin
1:40 - 1:50 SuperNeuroABM: Scalable, Biologically Realistic, and Hardware-Aware Neuromorphic Simulator – Xi Zhang, ORNL
1:50 - 2:10 Panel Discussion
Topic I: Energy Consequence of Biological Systems
2:10 - 2:30 KEYNOTE: From Dendrites to Waves: Multiplexed Codes for Energy-Efficient Cortical Information Processing – Krishna Jayant, PhD, Purdue University
2:30 - 2:40 A Theoretical Framework for Time, Space, and Energy Scaling in Neuromorphic Systems - Brad Aimone, SNL
2:40 - 2:50 Energetic Demands of Continual Learning in Biological Brains - Becket Ebitz
2:50 - 3:00
3:00 - 3:10 Large Multimodal Brain Models for Neuroscience and Computing – David Park/Shinjae Yoo, BNL
3:10 - 3:30 Panel Discussion
Topic J: Advances in Neuroscience relevant to Emerging Computing
3:30 - 3:50 KEYNOTE – Garr Kenyon (Overview DOE Neuromorphic Workshop), LANL
3:50 - 4:00 Energy efficiency of neuromorphic architectures for Generative AI - Frances Chance, SNL
4:00 - 4:10
4:10 - 4:20 Compositional associative memories support efficient, robust, and flexible computations – Christopher Kymn, UC Berkeley
4:20 - 4:30 Mapping Challenges of Connectomes for Performant Neuromorphic Simulation – Felix Wang, SNL
4:30 - 4:50 Panel Discussion
4:50 - 5:00 Day 2 closeout
Poster Session/Reception
5:30 - 7:30pm Poster Session and Refreshments
A-Graph: A Unified System Representation for Automated Cross-Stack Optimization - Di Wu
Catwalk Neuron for Efficient Temporal Neural Networks - Di Wu
Computing with Discontinuous Matter: Chain-Linked Nanostructures for Energy-Frugal Optimization and Analog Inference - Wenjie Zhou
Control of analog brain dynamics & learning as a computational principle for neuromorphic computing derived from the brains SWaP optimized multi-scale co-design principles - Kris Bouchard
Denoising with Equilibrium Propagation - Siddharth Mansingh
Efficient prediction in cultured neurons and humans - Sarah Marzen
Energy-Efficient Acceleration of Transformers with Stochastic Photonic Computing - Ishan Thakkar
Federated neuromorphic computing in distributed edge environments - Seung-Hwan Lim
Genesis: A Spiking Neuromorphic Accelerator with On-chip Continual Learning - Vedant Karia
GPU Support and Other Improvements for SuperNeuroMAT - Kevin Zhu, Prasanna Date
Hybrid Splat-and-Mesh Simulation for Rapid Humanoid Training in Isaac Sim - Wei Xu
In-DRAM Stochastic Computing for Energy-Efficient Acceleration of Transformer Neural Networks - Ishan Thakkar
Inverse-Designed Photonic Dot-Product Primitives for Energy-Efficient Neuromorphic Computing – Raktim Sarma
Reading and Computing with Light via Multiscale Nano-Architected Photonics - Yuanwei Li
Reliable Cryogenic Circuits in Unreliable Environments - George Michelogiannakis
Sample- and Modality‐Efficient Spatiotemporal Learning for Neuroscience and Robotics - Xihaier Luo
Sense-as-You-Go: A Neuromorphic Framework for Efficient Edge Sensing and Processing - Di Wu, Mohsen Rakhshan
Signal propagation dynamics across the Drosophila hemi-brain connectome modeled as a multiplex networks reveal a parallel-hierarchical (‘U-Net’) sensory-cogntive-motor architecture - Kris Bouchard
Spatial Mental Modeling from Limited Views - Manling Li
UQ-VarQA: Benchmarking and Characterizing NISQ Computers Through Uncertainty Quantification of Variational Quantum Algorithms - Qiang Guan
Thursday, February 5
8:30 - 8:45 Day 3 Welcome
Topic K: Energy Aspects of Quantum Computing
8:45 - 9:05 KEYNOTE: Quantum Computing Benchmarking Initiative – Mike Descour, SNL
9:05 - 9:15 Overview of Quantum Computing Systems – Kasia Kryzyzanowska, LANL
9:15 - 9:25 EnerQy: Energy Estimation for Quantum Computing – Di Wu, University of Central Florida
9:25 - 9:35 Energy-Aware Orchestration for Hybrid Quantum-Classical Computing: Towards Unified Telemetry and Cost Models – Ozgur Ozan Kilic, BNL
9:35 - 9:45 Quantum Enhanced Josephson Junction Field-Effect Transistors for Low-Energy Power-Efficient Microelectronics Applications – Wei Pan, SNL
9:45 - 10:05 Panel Discussion
Topic L: Thermodynamic Computing
10:05 - 10:25 KEYNOTE
10:25 - 10:35 Scalable Ising Solvers for Combinatorial Optimization – Shimeng Yu, Georgia Institute of Technology
10:35 - 10:45
10:45 - 10:55 Kinetic Inductors Enable Reversible Logic – Erik DeBenedictis,
10:55 - 11:05
11:05 - 11:25 Panel Discussion
11:25 - 11:35 *Break before Working Lunch Keynote*
11:35 - 12:35 FUNDER'S LUNCH
Robinson Pino (DOE ASCR)
Hal Greenwald (AFOSR)
Chiping Li (AFOSR)
Jim Lyke (Latin American Office for AFOSR)
Grace Hwang (NIH)
Hou Hung (ARL)
12:35 - 12:50 *Break*
Topic M: Macro- and micro solutions to HPC Energy budgets
12:50 - 1:10 KEYNOTE: Mapping neuro-inspired principles and motifs to on-chip AI in sensor networks and signal processing applications – Angel Yanguas-Gil, ANL
1:10 - 1:20 Chiplets already fuel the Exascale. Chiplet modularity is key for the next 100-1000x energy savings – Patricia Gonzalez-Guerrero, BNL
1:20 - 1:30 Thermal Management Opportunities from Exploding Energy Demands in Datacenters – Foluso Ladeinde, Stony Brook University
1:30 - 1:40
1:40 - 1:50 Energy Consequences of Machine Learning Inference on HPCs – Mohammed Atif, BNL
1:50 - 2:10 Panel Discussion
Topic N: Co-Design
2:10 - 2:30 KEYNOTE: A Full-Stack Neuromorphic System Implementation for Development and Benchmarking – Rajit Manohar, Yale University
2:30 - 2:40 Towards Efficient and Scalable Graph Solutions with Neuromorphic Learning – Shruti Kulkarni, ORNL
2:40 - 2:50 Fault-Tolerance as a Basis for Energy-Efficient Co-Design – Hemanth Kolla, SNL
2:50 - 3:00 A principled procedure for designing brain-derived SWaP optimized neuronal units for low-power neuromorphic analog computation and digital communication – Kris Bouchard, LBL
3:00 - 3:10 Predictive Quantum Simulations for State-of-the-Art and Beyond CMOS Device Technologies: Enabling the Full Stack Co-design of Novel Computing Systems – Juan Mendez, SNL
3:10 - 3:30 Panel Discussion
Topic L2: AI-guided Co-Design/ Digital Twins
3:30 - 3:50 KEYNOTE
3:50 - 4:00 Agentic AI for Accelerating Automated Co-design of Neuromorphic Systems – Yihui Ren, BNL
4:00 - 4:10 Neuromorphic Co-Discovery: Beyond Co-Design – Cale Crowder, SNL
4:10 - 4:20 Spike-based Agentic AI and Knowledge Graphs for Electronic Design Automation – Prasanna Date, ORNL
4:20 - 4:30
4:30 - 4:50 Panel Discussion
4:50 - 5:00 Day 3 closeout
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