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Published on: November 12, 2019
Minimizing the effect of process mismatch in a neuromorphic system using spike-timing-dependent adaptation
Katherine Cameron1, Alan Murray
1School of Engineering and Electronics, The University of Edinburgh, Edinburgh EH9 3JL, UK. K.Cameron@ed.ac.uk
IEEE Transactions on Neural Networks
|May 10, 2008
Summary
Spike-timing-dependent plasticity (STDP) effectively minimizes analog very large scale integration (VLSI) mismatch in depth-from-motion algorithms. STDP adaptation improves prediction accuracy and noise rejection, enhancing overall system performance.
Area of Science:
- Computational neuroscience
- Neuromorphic engineering
- Artificial intelligence
Background:
- Depth-from-motion algorithms rely on accurate spike prediction.
- Analog very large scale integration (VLSI) circuits suffer from transistor mismatch, degrading performance.
- Spike-timing-dependent plasticity (STDP) is a learning mechanism in neural systems.
Purpose of the Study:
- To investigate STDP's efficacy in mitigating VLSI mismatch effects.
- To enhance the noise rejection capabilities of depth-from-motion algorithms.
- To evaluate STDP's impact on spike prediction accuracy.
Main Methods:
- Implemented an STDP circuit to adapt spike prediction.
- Used prediction error as input for STDP adaptation.
- Tested circuitry with static and varying prediction times.
- Investigated the effect of noisy input spikes.
Main Results:
- STDP adaptation significantly reduced prediction errors caused by mismatch.
- The algorithm demonstrated improved performance under various conditions.
- Adaptation effectively minimized the impact of transistor mismatch.
- Noise rejection capabilities were enhanced by STDP.
Conclusions:
- STDP is a viable strategy for compensating analog VLSI mismatch.
- Adaptive circuitry using STDP improves the robustness of depth-from-motion systems.
- The findings support the use of STDP in neuromorphic hardware for improved reliability.
