P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications

7 March 2022

Papers citing "P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications"

7 / 7 papers shown

Title
ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNs Fang Chen Gourav Datta Mujahid Al Rafi Hyeran Jeon Meng Tang 93 1 0 06 Jun 2024
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision Gourav Datta Zeyu Liu Md. Abdullah-Al Kaiser Souvik Kundu Joe Mathai Zihan Yin Ajey P. Jacob Akhilesh R. Jaiswal P. Beerel 26 12 0 21 Dec 2022
Self-Attentive Pooling for Efficient Deep Learning Fang Chen Gourav Datta Souvik Kundu P. Beerel 79 6 0 16 Sep 2022
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator Chuteng Zhou F. García-Redondo Julian Büchel I. Boybat Xavier Timoneda Comas S. Nandakumar Shidhartha Das Abu Sebastian Manuel Le Gallo P. Whatmough 27 16 0 10 Nov 2021
A Reconfigurable Convolution-in-Pixel CMOS Image Sensor Architecture Rui Song Kejie Huang Zongsheng Wang Haibin Shen 19 25 0 09 Jan 2021
Benchmarking TinyML Systems: Challenges and Direction Colby R. Banbury Vijay Janapa Reddi Max Lam William Fu A. Fazel ... Jae-sun Seo Jeff Sieracki Urmish Thakker Marian Verhelst Poonam Yadav 109 228 0 10 Mar 2020
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 950 20,567 0 17 Apr 2017