Neuromorphic Intelligence: Circuits, Systems, and Architectures

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Track Chair

Kaijie Wei
Keio University

Neuromorphic intelligence is entering a decisive phase, driven by breakthroughs in brain‑inspired algorithms, emerging device technologies, and scalable architectures capable of real‑time, energy‑efficient learning. This track invites high‑quality contributions that push the boundaries of neuromorphic computing—from fundamental circuits and devices to full‑stack systems and large‑scale architectures. We welcome original research, visionary concepts, and practical implementations that advance the state of the art in neuromorphic engineering and its applications across sensing, robotics, edge intelligence, autonomous systems, and next‑generation AI.

Topics of interest include, but are not limited to:

Neuromorphic Circuits & Devices

Analog, digital, and mixed‑signal neuromorphic circuits
Emerging devices for synapses and neurons (RRAM, PCM, FeFET, memristors, spintronics, photonics)
Low‑power, event‑driven circuit design
On‑chip learning mechanisms and plasticity circuits

Neuromorphic Architectures & Systems

Scalable neuromorphic processors and multi‑core architectures
Event‑driven and asynchronous architectures
3D integration, in‑memory computing, and compute‑near‑memory systems
Hardware acceleration for spiking neural networks (SNNs) and brain‑inspired models
Neuromorphic SoCs for edge and embedded intelligence

Algorithms, Models, and Co‑Design

Learning algorithms for SNNs (STDP, R-STDP, backprop‑through‑time, local learning rules)
Bio‑inspired computation models and cognitive architectures
Hardware–algorithm co‑design and optimization
Tools, compilers, and simulation frameworks for neuromorphic systems

Applications of Neuromorphic Intelligence

Robotics, autonomous systems, and real‑time control
Event‑based vision, auditory processing, and multimodal sensing
Edge AI, IoT, and ultra‑low‑power inference
Brain–machine interfaces and computational neuroscience

Paper Submission

All papers must be submitted electronically through EDAS. Authors are encouraged to review the detailed submission instructions before uploading their manuscripts.

View Submission Guidelines