TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Weller, Dennis D.
A1  - Hefenbrock, Michael
A1  - Tahoori, Mehdi Baradaran
A1  - Aghassi-Hagmann, Jasmin
A1  - Beigl, Michael
T1  - Programmable Neuromorphic Circuit based on Printed Electrolyte-Gated Transistors
T2  - ASP-DAC 2020. 25th Asia and South Pacific Design Automation Conference : Proceedings
N2  - Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
Y1  - 2020
SN  - 2153-697X (Online)
SS  - 2153-697X (Online)
SN  - 2153-6961 (Print on Demand)
SS  - 2153-6961 (Print on Demand)
SN  - 978-1-7281-4123-7 (eBook)
SB  - 978-1-7281-4123-7 (eBook)
SN  - 978-1-7281-4124-4 (Print on Demand)
SB  - 978-1-7281-4124-4 (Print on Demand)
U6  - https://doi.org/10.1109/ASP-DAC47756.2020.9045211
DO  - https://doi.org/10.1109/ASP-DAC47756.2020.9045211
SP  - 446
EP  - 451
PB  - IEEE
ER  -