The TSP1 Neuromorphic Chip: Advancing Brain-Inspired Computing

Join Chris Eliasmith for an in-depth exploration of the TSP1 neuromorphic chip from Applied Brain Research. Learn about this groundbreaking hardware platform and its implications for brain-inspired computing.

The TSP1 Neuromorphic Chip: Advancing Brain-Inspired Computing

About This Workshop

Join us for an exciting workshop featuring Dr. Chris Eliasmith as he presents the TSP1 neuromorphic chip, a cutting-edge hardware platform developed by Applied Brain Research. This event will provide insights into how neuromorphic computing can bridge the gap between artificial intelligence and biological neural systems.

What You’ll Learn

In this workshop, Dr. Eliasmith will cover:

  • The TSP1 Architecture: An overview of the TSP1 chip’s unique design and capabilities
  • Brain-Inspired Computing: How the TSP1 embodies principles from neuroscience to create efficient, low-power computing solutions
  • Real-World Applications: Practical use cases where neuromorphic hardware like TSP1 excels, including edge computing, robotics, and adaptive systems
  • Integration with Nengo: How the TSP1 chip works seamlessly with the Nengo neural modeling framework
  • Performance and Efficiency: Comparisons with traditional computing architectures and insights into power consumption and speed

About the TSP1 Chip

The TSP1 (Temporal Semantic Pointer 1) is a neuromorphic processor designed to efficiently implement the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA). Developed by Applied Brain Research, the TSP1 chip represents a significant advancement in brain-inspired computing hardware, offering:

  • Ultra-low power consumption suitable for edge deployment
  • Real-time processing of complex neural computations
  • Scalable architecture for building large-scale brain models
  • Native support for temporal dynamics and structured representations

This hardware platform enables researchers and developers to deploy sophisticated cognitive models and neural networks in real-world applications where power efficiency and real-time performance are critical.

Who Should Attend

This workshop is ideal for:

  • Researchers in neuromorphic computing and computational neuroscience
  • Engineers working on edge AI and embedded systems
  • Developers interested in brain-inspired computing platforms
  • Students exploring neuromorphic hardware and neural modeling
  • Anyone curious about the future of efficient AI computing

Prerequisites

No specific prerequisites are required, though familiarity with neural networks and basic neuroscience concepts will enhance your understanding. Prior experience with Nengo is helpful but not necessary.

Resources

Registration

Registration details and the event link will be announced soon. Stay tuned for updates on how to join this exciting workshop!


This workshop is part of the Open Neuromorphic community’s ongoing series to showcase cutting-edge neuromorphic hardware and software platforms.

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Upcoming Workshops

The TSP1 Neuromorphic Chip: Advancing Brain-Inspired Computing
Chris Eliasmith, Danny Rosen
November 11, 2025
8:00 - 9:00 EST

About the Authors

Chris Eliasmith

Chris Eliasmith

Professor, Canada Research Chair in Theoretical Neuroscience, and co-creator of the Nengo neural simulator. Research focuses on large-scale brain modelling and neuromorphic engineering.
Danny Rosen

Danny Rosen

Danny Rosen is a Master’s student in Computer Engineering at Virginia Tech’s Innovation Campus in Alexandria, Virginia. He’s currently researching Spiking Neural Networks (SNNs) for edge-based signal processing.

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