Binarized Neural Network with Silicon Nanosheet Synaptic Transistors for Supervised Pattern Classification

Abstract

In the biological neural network, the learning process is achieved through massively parallel synaptic connections between neurons that can be adjusted in an analog manner. Recent developments in emerging synaptic devices and their networks can emulate the functionality of a biological neural network, which will be the fundamental building block for a neuromorphic computing architecture. However, on-chip implementation of a large-scale artificial neural network is still very challenging due to unreliable analog weight modulation in current synaptic device technology. Here, we demonstrate a binarized neural network (BNN) based on a gate-all-around silicon nanosheet synaptic transistor, where reliable digital-type weight modulation can contribute to improve the sustainability of the entire network. BNN is applied to three proof-of-concept examples: (1) handwritten digit classification (MNIST dataset), (2) face image classification (Yale dataset), and (3) experimental 3 × 3 binary pattern classifications using an integrated synaptic transistor network (total 9 × 9 × 2 162 cells) through a supervised online training procedure. The results consolidate the feasibility of binarized neural networks and pave the way toward building a reliable and large-scale artificial neural network by using more advanced conventional digital device technologies.

Impact of Synaptic Device Variations on Classification Accuracy in a Binarized Neural Network

“…By applying a supervised online training scheme, a set of multiple digital-type synaptic devices (buckets) were able to represent the analog synaptic weight. The BNN had an image classification capability that was verified by simulation and experiment 19 . However, our previous simulation was limited because the effect of synaptic device variations was ignored; the simulation was performed under the assumption that all synaptic devices in the system had equivalent characteristics without any variations.…”

“…In our previous work, we demonstrated a BNN and its supervised training scheme for an image classification application 19 . Briefly, Fig.…”

“…Therefore, if well-qualified conventional digital devices can contribute to a synaptic device, the aforementioned variability issues from memristors can be addressed. We have demonstrated a binarized neural network (BNN) in our previous study 19 , in which the synaptic device was a mature digital-type switchable device—a gate-all-around (GAA) silicon nanosheet transistor. By applying a supervised online training scheme, a set of multiple digital-type synaptic devices (buckets) were able to represent the analog synaptic weight.…”

Electrolyte‐Gated Transistor Array (20 × 20) with Low‐Programming Interference Based on Coplanar Gate Structure for Unsupervised Learning

“…Therefore, when programming a specific device within the array, other devices in the same column may also experience programming interference (Figure 3a). [66,67] This is disadvantageous for subsequent differential computing tasks. To address this issue, we devised an independent programming method based on the EGTs array design.…”

Double-Gated Asymmetric Floating-Gate-Based Synaptic Device for Effective Performance Enhancement Through Online Learning

“…Neuromorphic systems are rising candidates for the next generation computing system due to their massively parallel data processing capability and minimal power consumption [1][2][3][4][5][6][7][8]. Various researchers have implemented neuromorphic systems using their unique methods and performing machine learning tasks such as pattern recognition or image denoising [9][10][11][12][13]. Neuromorphic systems consist of neuron circuits and synaptic devices, and their implementation differ depending on the specific combination of incorporated circuits and devices.…”