A population-level approach to temperature robustness in neuromorphic systems
| Autor: | Ben Varkey Benjamin, Andrew Gilbert, Kwabena Boahen, Terrence C. Stewart, Eric Kauderer-Abrams, Aaron R. Voelker |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
education.field_of_study
Computer science 020208 electrical & electronic engineering Population Robust optimization 02 engineering and technology Function (mathematics) 03 medical and health sciences 0302 clinical medicine Neuromorphic engineering Control theory Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Range (statistics) education 030217 neurology & neurosurgery |
| Zdroj: | ISCAS |
| DOI: | 10.1109/iscas.2017.8050985 |
| Popis: | We present a novel approach to achieving temperature-robust behavior in neuromorphic systems that operates at the population level, trading an increase in silicon-neuron count for robustness across temperature. Our silicon neurons' tuning curves were highly sensitive to temperature, which could be decoded from a 400-neuron population with a precision of 0.07° C. We overcame this temperature-sensitivity by combining methods from robust optimization theory with the Neural Engineering Framework. We developed two algorithms and compared their temperature-robustness across a range of 2° C by decoding one period of a sinusoid-like function from populations with 25 to 800 neurons. We find that 560 neurons are required to achieve the same precision across this temperature range as 35 neurons achieved at a single temperature. |
| Databáze: | OpenAIRE |
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