We have designed a fully-integrated analog CMOS cognitive image sensor based on a two-layer artificial neural network and targeted to low-resolution image classification. We have used a single poly 180 nm CMOS process technology, which includes process modules for realizing the building blocks of the CMOS image sensor. Our design includes all the analog sub-circuits required to perform the cognitive sensing task, from image sensing to output classification decision. The weights of the network are stored in single-poly floating-gate memory cells, using a single transistor per analog weight. This enables the classifier to be intrinsically reconfigurable, and to be trained for various classification problems, based on low-resolution images. As a case study, the classifier capability is tested using a low-resolution version of the MNIST dataset of handwritten digits. The circuit exhibits a classification accuracy of 87.8%, that is comparable to an equivalent software implementation operating in the digital domain with floating point data precision, with an average energy consumption of 6 nJ per inference, a latency of 22.5 mu text{s} and a throughput of up to 133.3 thousand inferences per second.

All-Analog Silicon Integration of Image Sensor and Neural Computing Engine for Image Classification

Zambrano Benjamin;Garzon Esteban;Lanuzza M.;
2022

Abstract

We have designed a fully-integrated analog CMOS cognitive image sensor based on a two-layer artificial neural network and targeted to low-resolution image classification. We have used a single poly 180 nm CMOS process technology, which includes process modules for realizing the building blocks of the CMOS image sensor. Our design includes all the analog sub-circuits required to perform the cognitive sensing task, from image sensing to output classification decision. The weights of the network are stored in single-poly floating-gate memory cells, using a single transistor per analog weight. This enables the classifier to be intrinsically reconfigurable, and to be trained for various classification problems, based on low-resolution images. As a case study, the classifier capability is tested using a low-resolution version of the MNIST dataset of handwritten digits. The circuit exhibits a classification accuracy of 87.8%, that is comparable to an equivalent software implementation operating in the digital domain with floating point data precision, with an average energy consumption of 6 nJ per inference, a latency of 22.5 mu text{s} and a throughput of up to 133.3 thousand inferences per second.
Analog neural network
cognitive image sensor
neuromorphic engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/338052
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