Publications by authors named "Runxiao Shi"
A Sliding-Kernel Computation-In-Memory Architecture for Convolutional Neural Network.
Adv Sci (Weinh)
December 2024
Article Synopsis
- - The SKCIM architecture features two layers: one for memory and artificial synapses for neuromorphic tasks, and the other for storing and using convolutional kernel matrices.
- - It utilizes low-temperature metal-oxide thin-film transistor technology to seamlessly integrate multiple components, leading to a significant decrease in memory access by 88% compared to existing systems.
- - A demonstration showcases a 5-layer SKCIM-based convolutional neural network achieving over 95% accuracy in classifying handwritten digits from the MNIST dataset.
The application of a versatile, low-temperature thin-film transistor (TFT) technology is presently described as the implementation on a flexible substrate of an analog front-end (AFE) system for the acquisition of bio-potential signals. The technology is based on semiconducting amorphous indium-gallium-zinc oxide (IGZO). The AFE system consists of three monolithically integrated constituent components: a bias-filter circuit with a bio-compatible low cut-off frequency of ≈1 Hz, a 4-stage differential amplifier offering a large gain-bandwidth product of ≈955 kHz, and an additional notch filter exhibiting over 30 dB suppression of the power-line noise.
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