Founder-led research initiative • Early-stage architecture validation

NRN — Neuromorphic Research Node

Analog neuromorphic circuits for edge biosignal intelligence. Exploring subthreshold CMOS neuron and synapse architectures, and practical integration with embedded inference pipelines.

CollaborateResearch FocusCurrent StageContact

Mission

NRN is an independent research initiative exploring ultra-low-power analog neuron circuits for edge intelligence systems.

The focus is on designing subthreshold CMOS neuromorphic architectures capable of processing biosignals (e.g., EMG) directly at the sensor edge, reducing power consumption and latency compared to conventional digital pipelines. NRN investigates circuit-level implementations of spiking neuron models and their integration into practical embedded systems.

Research Focus — Analog Neuron Design

DPI / current-mode integratorsLIF implementationsSynaptic circuits
  • Subthreshold Differential Pair Integrator (DPI) architectures
  • Leaky-Integrate-and-Fire (LIF) implementations
  • Current-mode synaptic blocks and biasing strategies
  • Power–noise trade-off optimization
  • PVT robustness analysis and corner coverage

Research Focus — IC Architecture

ScalabilityLayout-aware designEnergy per spike
  • Single-neuron validation → multi-neuron scalability exploration
  • Layout-aware analog design for array-readiness
  • Corner simulations (TT / SS / FF) and sensitivity testing
  • Energy per spike / event characterization
  • Roadmapping toward array-level integration

Research Focus — Edge Integration

EMG pipelineFPGA spike processingHardware-aware SNN
  • EMG signal acquisition pipeline and preprocessing
  • FPGA-based spike processing and inference interfaces
  • Analog–digital co-design exploration
  • Hardware-aware SNN deployment strategies

System Sketch (Concept)

EMG SensorAnalog Front-End(conditioning / biasing)Neuromorphic Neuron Coresubthreshold neuron + synapse blocksFPGAInferenceclassification

This diagram is conceptual; the near-term goal is validating the neuron core and measurement methodology before array-level integration.

Current Stage

NRN is currently in early-stage architectural development focused on validating core building blocks before array-level expansion.

  • Circuit-level neuron simulation in CMOS
  • Power and noise characterization methodology
  • Exploration of scalable synapse integration
  • EMG acquisition system prototyping
  • System-level architecture definition (interfaces, outputs, measurements)

Long-Term Vision

NRN explores ultra-low-power neuromorphic processing for wearable biosignal systems and analog-dominant architectures for edge AI, with an emphasis on efficient co-design of sensing, computation, and inference.

Future directions may include tape-out exploration, academic publication, or commercialization pathways depending on technical maturity and research outcomes.

Collaboration

NRN welcomes technical collaboration in the following areas:

  • Analog IC design and layout
  • Mixed-signal integration
  • FPGA-based neuromorphic inference
  • SNN modeling and training
  • Biosignal processing (EMG/EEG)

Note: NRN is currently a founder-led research initiative. Contributions are structured as collaborative research efforts; future commercialization, if pursued, would involve separate agreements.

Founder

Paul Adu

M.S. Electrical & Computer Engineering • Research focus: Analog VLSI, Neuromorphic IC Design, Edge AI Systems

Email: pbadu816@gmail.com • LinkedIn: Paul Adu