학술논문

On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography
Document Type
Periodical
Source
IEEE Journal of Selected Topics in Quantum Electronics IEEE J. Select. Topics Quantum Electron. Selected Topics in Quantum Electronics, IEEE Journal of. 30(1: Single-Photon Technologies and Applications):1-13 Jan, 2024
Subject
Engineered Materials, Dielectrics and Plasmas
Photonics and Electrooptics
Artificial neural networks
Neurons
System-on-chip
Positron emission tomography
Codes
Timing
Image reconstruction
Artificial neural network (ANN)
ANN-reconfigurability
feed-forward ANN
genetic algorithm
time-to-digital converter (TDC)
position reconstruction
Language
ISSN
1077-260X
1558-4542
Abstract
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 × 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.