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Related Experiment Video

Updated: Sep 8, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots.

Songchen Ma¹, Jing Pei¹, Weihao Zhang¹

¹Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.

Science Robotics

|June 15, 2022

Summary

This summary is machine-generated.

A new neuromorphic computing chip, TianjicX, enables mobile robots to run multiple artificial intelligence tasks concurrently with significantly reduced latency and power consumption. This innovation is crucial for efficient, real-time robotic applications.

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Last Updated: Sep 8, 2025

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Area of Science:

Robotics
Artificial Intelligence
Neuromorphic Computing

Background:

Mobile robots require efficient local processing for complex, dynamic environments.
Current hardware struggles with computationally intensive algorithms, leading to high latency and power demands.

Purpose of the Study:

To introduce TianjicX, a novel neuromorphic computing hardware for concurrent execution of diverse neural network models in robotics.
To develop the Rivulet model for bridging robotic requirements and hardware implementation with spatiotemporal elasticity.

Main Methods:

Designed TianjicX, a 28nm neuromorphic chip supporting concurrent, cross-paradigm neural network execution.
Developed the Rivulet model for elastic resource allocation and task scheduling.
Integrated TianjicX and Rivulet into the Tianjicat mobile robot for multi-task execution.

Main Results:

TianjicX demonstrated true concurrent execution of multiple neural network models for robotics.
The Tianjicat robot successfully performed concurrent tasks like object recognition and obstacle avoidance.
Achieved a 79.09x reduction in latency and 50.66% reduction in dynamic power compared to NVIDIA Jetson TX2.

Conclusions:

TianjicX offers a low-latency, high-efficiency solution for multitask mobile robots.
Spatiotemporal elasticity and the Rivulet model are key to enabling advanced robotic AI.
This hardware innovation significantly advances the capabilities of real-time robotic systems.