TY  - BOOK
AB  - We use a large-scale analog neuromorphic system to encode the hidden-layer activations of a single-layer feed forward network with random weights. The random activations of the network are implemented using the device mismatch inherent to analog circuits. We show that these activations produced by analog VLSI implementations of integrate and fire neurons are suited to solve multi dimensional, non linear regression tasks. Exploitation of the device mismatch eliminates the storage requirements for the random network weights.
DA  - 2015
DO  - 10.1109/BioCAS.2015.7348416
KW  - Computer architecture
KW  - Feeds
KW  - Function approximation
KW  - Hardware
KW  - Neuromorphics
KW  - Neurons
KW  - Standards
KW  - Extreme Learning Machine
KW  - Feed Forward Neural Networks With Random Weights
KW  - Neuromorphic
KW  - aVLSI
KW  - device mismatch
LA  - eng
PY  - 2015
TI  - Device Mismatch in a Neuromorphic System Implements Random Features for Regression
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27670399
Y2  - 2024-11-22T01:08:21
ER  -