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The Track on Architectures for Large‑Scale Spiking Neural Systems invites high‑quality submissions that advance the theory, design, implementation, and evaluation of next‑generation spiking neural computing platforms. As neuromorphic intelligence rapidly evolves toward brain‑scale, event‑driven, and energy‑efficient computation, this track aims to bring together researchers from computer architecture, VLSI/3D‑IC design, computational neuroscience, machine learning, and embedded systems to explore emerging architectural paradigms for scalable spiking neural processing.
Scope and Topics of Interest
We welcome original contributions, including (but not limited to):
1. Architectural Foundations
- Scalable architectures for large‑scale SNNs
- Event‑driven, asynchronous, and mixed‑signal neuromorphic processors
- 2D/3D integrated architectures for high‑density synaptic memory
- Crossbar‑based, RRAM‑based, PCM‑based, and memristive SNN accelerators
- Architectures for online learning, STDP, R-STDP, and local plasticity rules
2. Communication & Interconnects
- Low‑latency, high‑bandwidth interconnects for spike‑based communication
- 3D NoC, TSV‑aware, and fault‑tolerant routing for neuromorphic systems
- Event‑driven communication protocols and spike‑routing fabrics
- Scalable multi‑chip and wafer‑scale neuromorphic interconnects
3. System‑Level Design
- End‑to‑end system architectures for brain‑scale SNNs
- Memory hierarchies and synaptic storage optimization
- Power‑aware, thermal‑aware, and reliability‑aware design methodologies
- Hardware–software co‑design for large‑scale neuromorphic platforms
4. Algorithms, Models, and Co‑Optimization
- Co‑design of SNN models and hardware architectures
- Mapping, partitioning, and scheduling of large SNN workloads
- Learning algorithms optimized for hardware constraints
- Neuromorphic compilation, quantization, and model compression
5. Applications and Benchmarks
- Real‑time robotics, autonomous systems, and sensorimotor control
- Edge and cloud‑scale neuromorphic computing applications
- Bio‑inspired cognitive systems and brain‑machine interfaces
- Benchmarking methodologies and performance evaluation frameworks
