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  4. On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography
 
research article

On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography

Muntean, Andrada Alexandra  
•
Shoshan, Yonatan
•
Yuzhaninov, Slava
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January 1, 2024
Ieee Journal Of Selected Topics In Quantum Electronics

Smarty is a fully-reconfigurable on-chip feed-forward artificial neural network (ANN) with ten integrated time-to-digital converters (TDCs) designed in a 16 nm FinFET CMOS technology node. The integration of TDCs together with an ANN aims to reduce system complexity and minimize data throughput requirements in positron emission tomography (PET) applications. The TDCs have an average LSB of 53.5 ps. The ANN is fully reconfigurable, the user being able to change its topology as desired within a set of constraints. The chip can execute 363 MOPS with a maximum power consumption of 1.9 mW, for an efficiency of 190 GOPS/W. The system performance was tested in a coincidence measurement setup interfacing Smarty with two groups of five 4 mm x 4 mm analog silicon photomultipliers (A-SiPMs) used as inputs for the TDCs. The ANN succesfully distinguished between six different positions of a radioactive source placed between the two photodetector arrays by solely using the TDC timestamps.

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Type
research article
DOI
10.1109/JSTQE.2023.3346957
Web of Science ID

WOS:001179737500002

Author(s)
Muntean, Andrada Alexandra  
Shoshan, Yonatan
Yuzhaninov, Slava
Ripiccini, Emanuele  
Bruschini, Claudio  
Fish, Alexander
Charbon, Edoardo  
Date Issued

2024-01-01

Publisher

Ieee-Inst Electrical Electronics Engineers Inc

Published in
Ieee Journal Of Selected Topics In Quantum Electronics
Volume

30

Issue

1

Article Number

7600213

Subjects

Technology

•

Physical Sciences

•

Artificial Neural Network (Ann)

•

Ann-Reconfigurability

•

Feed-Forward Ann

•

Genetic Algorithm

•

Time-To-Digital Converter (Tdc)

•

Position Reconstruction

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
AQUA  
FunderGrant Number

Swiss National Science Foundation

Available on Infoscience
April 3, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/206877
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