Search Results for author: Shimeng Yu

Found 5 papers, 1 papers with code

DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training

2 code implementations13 Mar 2020 Xiaochen Peng, Shanshi Huang, Hongwu Jiang, Anni Lu, Shimeng Yu

Our prior work (DNN+NeuroSim V1. 1) was developed to estimate the impact of reliability in synaptic devices, and analog-to-digital converter (ADC) quantization loss on the accuracy and hardware performance of inference engines.

Quantization

High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS

no code implementations16 Sep 2019 Shihui Yin, Xiaoyu Sun, Shimeng Yu, Jae-sun Seo

In this work, we demonstrate a scalable RRAM based in-memory computing design, termed XNOR-RRAM, which is fabricated in a 90nm CMOS technology with monolithic integration of RRAM devices between metal 1 and 2.

Edge-computing

Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

no code implementations23 May 2018 Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.

Device and System Level Design Considerations for Analog-Non-Volatile-Memory Based Neuromorphic Architectures

no code implementations25 Dec 2015 Sukru Burc Eryilmaz, Duygu Kuzum, Shimeng Yu, H. -S. Philip Wong

This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses.

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