J
Jiayoon Zhiyu Ru
Researcher at University of Twente
Publications - 7
Citations - 97
Jiayoon Zhiyu Ru is an academic researcher from University of Twente. The author has contributed to research in topics: Computer science & Spiking neural network. The author has an hindex of 1, co-authored 1 publications receiving 68 citations.
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Journal ArticleDOI
A High-Linearity Digital-to-Time Converter Technique: Constant-Slope Charging
TL;DR: This paper proposes constant-slope charging as a method to realize a DTC with intrinsically better integral non-linearity (INL) compared to the popular variable-Slope method.
Proceedings ArticleDOI
An 82nW 0.53pJ/SOP Clock-Free Spiking Neural Network with 40µs Latency for AloT Wake-Up Functions Using Ultimate-Event-Driven Bionic Architecture and Computing-in-Memory Technique
Ying Liu,Zhixuan Wang,Wei He,Linxiao Shen,Yihan Zhang,Peiyu Chen,Meng Wu,Hao Zhang,Peng Zhou,Jinguang Liu,Guangyu Sun,Jiayoon Zhiyu Ru,Le Ye,Ru Huang +13 more
TL;DR: Human brain is a natural ultimate-event-driven (UED) system with low power and real-time response-ability, thanks to the asynchronous propagation and processing of spikes, which gives natural event-driven property.
Proceedings ArticleDOI
Single-Mode CMOS 6T-SRAM Macros With Keeper-Loading-Free Peripherals and Row-Separate Dynamic Body Bias Achieving 2.53fW/bit Leakage for AIoT Sensing Platforms
Yihan Zhang,Chang Xue,Xiao Wang,Tianyi Liu,Jihang Gao,Peiyu Chen,Jinguang Liu,Linan Sun,Linxiao Shen,Jiayoon Zhiyu Ru,Le Ye,Ru Huang +11 more
TL;DR: Artificial-intelligence-of-things based sensing platforms aim to extend this concept further by using on-chip neural networks to detect valid events at the edge node, further reducing network traffic and overall power consumption by limiting the transmission of invalid events.
Proceedings ArticleDOI
7.8 A 22nm Delta-Sigma Computing-In-Memory (Δ∑CIM) SRAM Macro with Near-Zero-Mean Outputs and LSB-First ADCs Achieving 21.38TOPS/W for 8b-MAC Edge AI Processing
Peiyu Chen,Meng Wu,Wentao Zhao,Jiajia Cui,Zhixuan Wang,Yadong Zhang,Qijun Wang,Jiayoon Zhiyu Ru,Linxiao Shen,Tianyu Jia,Yufei Ma,Le Ye,Ru Huang +12 more
TL;DR: In this paper , the authors explored the sparsity of DNNs to improve energy efficiency, but the trend of employing non-sparse activation functions, e.g., leaky ReLU, degrade the benefits of leveraging sparsity, and the redundant unchanged input features in analog CIM still consume massive amount of dynamic power.
An Information-Aware Adaptive Data Acquisition System using Level-Crossing ADC with Signal-Dependent Full Scale and Adaptive Resolution for IoT Applications
TL;DR: In this article , the authors proposed an information-aware adaptive data acquisition (ADA) system for the Internet of Things (IoT) applications, which can obtain valid information adaptively thanks to signal-dependent full-scale feature tracks the amplitude-domain activity of the event, and level-crossing (LC) ADC with slope detector delivers the timedomain activity.