The TSP1 Neural Network Accelerator Chip: Advancing Brain-Inspired Computing

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

About This Workshop

Join us for an exciting workshop featuring Dr. Chris Eliasmith as he presents the TSP1 (Time Series Processor 1) neural network accelerator chip — a cutting-edge hardware platform developed by Applied Brain Research.
This event will provide insights into how brain-inspired computing can set world records in efficiency for AI applications.

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 neural accelerator hardware like TSP1 excels, including edge computing, robotics, and adaptive systems
  • Performance and Efficiency: Comparisons with traditional computing architectures and insights into power consumption and speed

About the TSP1 Chip

The TSP1 (Time Series Processor 1) represents a significant advancement in brain-inspired computing, offering:

  • Ultra-low power consumption suitable for edge deployment
  • Real-time processing of complex neural computations
  • Scalable architecture for building large-scale AI applications
  • Native support for temporal dynamics and time series processing

This hardware platform enables researchers and developers to deploy sophisticated AI applications 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 neural computing and efficient AI
  • Engineers working on edge AI and embedded systems
  • Developers interested in brain-inspired computing platforms
  • Students exploring neural accelerator hardware and time series modeling
  • Anyone curious about the future of efficient AI computing

Speaker

Chris Eliasmith, Professor and Canada Research Chair in Theoretical Neuroscience, and CTO at Applied Brain Research.
His research focuses on large-scale brain modelling, neural dynamics, efficient AI, and brain-inspired computing.

Resources

Registration

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

Social share preview for The TSP1 Neural Network Accelerator Chip: Advancing Brain-Inspired Computing

Upcoming Workshops

No workshops are currently scheduled. Check back soon for new events!

Are you an expert in a neuromorphic topic? We invite you to share your knowledge with our community. Hosting a workshop is a great way to engage with peers and share your work.

About the Speakers

Chris Eliasmith

Chris Eliasmith

Professor, Canada Research Chair in Theoretical Neuroscience, and CTO at Applied Brain Research. Research focuses on large-scale brain modelling, neural dynamics, efficient AI, 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.

Inspired? Share your work.

Share your expertise with the community by speaking at a workshop, student talk, or hacking hour. It’s a great way to get feedback and help others learn.

Related Workshops

The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

The ELM Neuron: An Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks

Aaron tells us about the Expressive Leaky Memory (ELM) neuron model, a biologically inspired phenomenological model of a cortical neuron.

Spyx Hackathon: Speeding up Neuromorphic Computing

Spyx Hackathon: Speeding up Neuromorphic Computing

Explore the power of Spyx in a hands-on hackathon session and dive into the world of neuromorphic frameworks with Kade Heckel.

C-DNN and C-Transformer: mixing ANNs and SNNs for the best of both worlds

C-DNN and C-Transformer: mixing ANNs and SNNs for the best of both worlds

Join us for a talk by Sangyeob Kim, Postdoctoral researcher at KAIST, on designing efficient accelerators that mix SNNs and ANNs.