AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator

10 November 2021

Papers citing "AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator"

7 / 7 papers shown

Title
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities James Read Ming-Yen Lee Wei-Hsing Huang Yuan-Chun Luo A. Lu Shimeng Yu 27 0 0 05 May 2025
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture Nazareno Bruschi Giuseppe Tagliavini Angelo Garofalo Francesco Conti I. Boybat Luca Benini D. Rossi 17 2 0 23 Nov 2022
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models Omobayode Fagbohungbe Lijun Qian 19 0 0 07 May 2022
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications Gourav Datta Souvik Kundu Zihan Yin R. T. Lakkireddy Joe Mathai A. Jacob P. Beerel Akhilesh R. Jaiswal 16 36 0 07 Mar 2022
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review Phu Khanh Huynh M. L. Varshika A. Paul Murat Isik Adarsha Balaji Anup Das 23 36 0 17 Feb 2022
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks Angelo Garofalo G. Ottavi Francesco Conti G. Karunaratne I. Boybat Luca Benini D. Rossi 14 30 0 04 Jan 2022
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,214 0 17 Apr 2017