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The Track on In‑Memory and Event‑Driven Computing Architectures welcomes original research contributions that push the boundaries of computation beyond the traditional von Neumann model. As data‑centric workloads, neuromorphic intelligence, and edge‑driven applications continue to grow, new architectures that tightly integrate memory, computation, and event‑driven processing are becoming essential. This track brings together researchers from computer architecture, circuits, emerging devices, neuromorphic engineering, and embedded systems to explore breakthroughs enabling ultra‑efficient, low‑latency, and massively parallel computing.
We invite submissions including, but not limited to, the following areas:
1. In‑Memory Computing Architectures
- Analog, digital, and mixed‑signal in‑memory computing (IMC) designs
- Compute‑in‑memory (CIM) accelerators for AI, SNNs, and data‑intensive workloads
- RRAM, PCM, FeFET, MRAM, and emerging NVM‑based IMC architectures
- Crossbar arrays, bit‑cell designs, and peripheral circuit innovations
- Precision, noise, variability, and reliability modeling for IMC systems
2. Event‑Driven and Neuromorphic Architectures
- Event‑driven processors and asynchronous computing architectures
- Spiking neural network (SNN) accelerators and neuromorphic cores
- Local learning rules (STDP, R-STDP, Hebbian) implemented in hardware
- Event‑based sensor integration (DVS, auditory, tactile, olfactory)
- Ultra‑low‑power architectures for real‑time, always‑on intelligence
3. Memory–Compute–Communication Co‑Design
- Unified memory and compute fabrics for event‑driven workloads
- 3D‑IC and chiplet‑based integration for IMC and neuromorphic systems
- Interconnects, NoCs, and spike‑routing fabrics optimized for event‑driven traffic
- Thermal, power, and reliability considerations in tightly coupled architectures
4. Algorithms, Mapping, and System Software
- Algorithm–architecture co‑design for IMC and event‑driven systems
- Compilation, mapping, and scheduling for large‑scale SNNs and IMC workloads
- Model compression, quantization, and sparsity exploitation
- Software frameworks and programming models for event‑driven computing
5. Applications and Benchmarks
- Edge AI, robotics, autonomous systems, and sensorimotor control
- Biomedical and brain‑machine interface applications
- Real‑time perception, anomaly detection, and adaptive control
- Benchmarking methodologies and evaluation metrics for IMC and event‑driven systems
